Taking a functional genomics approach in molecular medicine

Genome-scale modeling and in silico analysis of mouse cell metabolic network

Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting.

Glycogen synthase kinase‐3 is required for optimalde novosynthesis of inositol

Studies have shown that the inositol biosynthetic pathway and the enzyme glycogen synthase kinase-3 (GSK-3) are targets of the mood-stabilizing drugs lithium and valproate. However, a relationship between these targets has not been previously described. We hypothesized that GSK-3 may play a role in inositol synthesis, and that loss of GSK-3 may lead to inositol depletion, thus providing a mechanistic link between the two drug targets. Utilizing a yeast Saccharomyces cerevisiae gsk-3Delta quadruple-null mutant, in which all four genes encoding homologues of mammalian GSK-3 are disrupted, we tested the hypothesis that GSK-3 is required for de novo inositol biosynthesis. The gsk-3Delta mutant exhibited multiple features of inositol depletion, including defective growth in inositol-lacking medium, decreased intracellular inositol, increased INO1 and ITR1 expression, and decreased levels of phosphatidylinositol. Treatment of wild-type cells with a highly specific GSK-3 inhibitor led to a significant increase in INO1 expression. Supplementation with inositol alleviated the temperature sensitivity of gsk-3Delta. Activity of myo-inositol-3 phosphate synthase, the rate-limiting enzyme in inositol de novo biosynthesis, was decreased in gsk-3Delta. These results demonstrate for the first time that GSK-3 is required for optimal myo-inositol-3 phosphate synthase activity and de novo inositol biosynthesis, and that loss of GSK-3 activity causes inositol depletion.

Clathrin heavy and light chain isoforms originated by independent mechanisms of gene duplication during chordate evolution

In humans, there are two isoforms each of clathrin heavy chain (CHC17 and CHC22) and light chain (LCa and LCb) subunits, all encoded by separate genes. CHC17 forms the ubiquitous clathrin-coated vesicles that mediate membrane traffic. CHC22 is implicated in specialized membrane organization in skeletal muscle. CHC17 is bound and regulated by LCa and LCb, whereas CHC22 does not functionally interact with either light chain. The imbalanced interactions between clathrin subunit isoforms suggest a distinct evolutionary history for each isoform pair. Phylogenetic and sequence analysis placed both heavy and light chain gene duplications during chordate evolution, 510-600 million years ago. Genes encoding CHC22 orthologues were found in several vertebrate species, with only a pseudogene present in mice. Multiple paralogons surrounding the CHC genes (CLTC and CLTD) were identified, evidence that genomic or large-scale gene duplication produced the two CHC isoforms. In contrast, clathrin light chain genes (CLTA and CLTB) apparently arose by localized duplication, within 1-11 million years of CHC gene duplication. Analysis of sequence divergence patterns suggested that structural features of the CHCs were maintained after gene duplication, but new interactions with regulatory proteins evolved for the CHC22 isoform. Thus, independent mechanisms of gene duplication expanded clathrin functions, concomitant with development of neuromuscular sophistication in chordates.

Use of high throughput genomic tools for the study of endothelial cell biology

The endothelium is an active, dynamic and heterogeneous organ. It lines the vessels in every organ system and regulates diverse and important biological functions. Over the past several years researchers have gained enormous insights into endothelial cell function in physiological processes such as coagulation and vascular reactivity, and pathophysiological disease states such as inflammation and atherosclerosis. Despite our expanding knowledge of endothelial cell biology, the molecular mechanisms underlying these functions remain largely unknown. The newly developed high throughput genomic tools and accompanying analytical methods provide powerful approaches for identifying new endothelial cell genes and characterizing their role in health and disease. Here, we review some of the recent genomics and proteomic advances that are providing new methodologies for endothelial cell and vascular biology research.

Applications of eTag™ assay platform to systems biology approaches in molecular oncology and toxicology studies

We have developed a universal eTag trade mark multiplex assay platform that can be uniquely applied to survey the molecule profiles of biologic systems in sub-global large-scale analyses. The effectiveness of eTag trade mark assays when applied to focused system biology studies in molecular oncology and predictive toxicology is herein described while reviewing the current methods commonly used. The multi-analyte and multi-parameter assay approach for parallel analysis will form the basis of an emerging paradigm of multiplexed molecular profiling for signaling pathway networks and various aspects of drug development processes.

Functional genomics of fibroblasts

PURPOSE OF REVIEW

Successful analysis of the pathophysiology of rheumatoid arthritis requires the functional understanding of interactions between different cell types and the cell matrix, intracellular signaling pathways, as well as between cartilage, bone, and synovium in rheumatoid arthritis. During the review period, molecular biology has provided and used a growing number of tools to screen the genome such as gene and protein chips, haplotype analysis, and single nucleotide polymorphism analysis, resulting in various novel findings with considerable impact on the overall understanding of rheumatoid arthritis.

RECENT FINDINGS

Key issues that have been addressed and elucidated by numerous research groups are the regulation and modulation of synovial fibroblast metabolism and activation by proinflammatory cytokines and chemokines. In addition, examination of adhesion processes and neoangiogenesis has revealed new insights into the interaction network between rheumatoid synovial fibroblasts and the surrounding matrix and cells. Finally, a more detailed view of activation of these fibroblasts has been obtained by analysis of the molecular balance between cellular activation and regulation of apoptosis.

SUMMARY

Although high throughput molecular analysis methods provided an ample amount of novel data, it needs to be stressed that a one-method approach of gene expression (eg, by array analysis) is not sufficient to validate the gene/gene product as a new therapeutic target. Therefore, the next steps are the so-called functional genomics or functionomics, which intend to reveal relations between the obtained data and to unveil their interactions for a better understanding of the pathogenesis and the mechanisms that are operative in rheumatoid arthritis.

Functional Genomics Approaches in Arthritis

The post-genomic era of functional genomics and target validation will allow us to narrow the bridge between clinically correlative data and causative data for complex diseases, such as arthritis, for which the etiological agent remains elusive. The availability of human and other annotated genome sequences, and parallel developments of new technologies that allow analysis of minute amounts of human and animal cells (peripheral blood cells and infiltrating cells) and tissues (synovium and cartilage) under different pathophysiological conditions, has facilitated high-throughput gene mining approaches that can generate vast amounts of clinically correlative data. Characterizing some of the correlative/causative genes will require reverting to the hypothesis-driven, low throughput method of complementary experimental biology using genomic approaches as a tool. This will include in silico gene expression arrays, genome-wide scans, comparative genomics using various animal models (such as rodents and zebrafish), bioinformatics and a team of well trained translational scientists and physicians. For the first time, the "genomic tools" will allow us to analyze small amounts of surgical samples (such as needle biopsies) and clinical samples in the context of the whole genome. Preliminary genomic analysis in osteoarthritis has already resurrected the debate on the semantic issues in the definition of inflammation. Further analyses will not only facilitate the development of unbiased hypotheses at the molecular level, but also assist us in the identification and characterization of novel targets and disease markers for pharmacological intervention, gene therapy, and diagnosis.

Genes modulating chemical breathing control: lessons from mutant animals

Genetic factors influence breathing control. Respiratory phenotypes of mutant mice may help to better understand these factors. Congenital central hypoventilation syndrome (CCHS) is a rare disorder defined as failure of chemical control of breathing causing central alveolar hypoventilation, especially during sleep. A genetic basis for CCHS is supported by several arguments, mainly the identification, in a few CCHS patients, of heterozygous mutations of genes contributing to neural crest cell development, namely, genes involved in the endothelin and c-ret pathways. Furthermore, plethysmography studies of the respiratory phenotypes of newborn heterozygous mutant mice have shown that genes in both pathways are involved in breathing control at birth. Nevertheless, no single gene mutation in newborn mice reproduces the human CCHS phenotype. Avenues for future research into the genetics of CCHS include (i) testing of mutant newborn mice for genes in other pathways and (ii) use of microarrays to identify gene clusters that should be associated with abnormal chemical breathing control.

Functional genomics of sleep

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.

A need for a 'whole-istic functional genomics' approach in complex human diseases: arthritis

'Genomic tools', such as gene/protein chips, single nucleotide polymorphism and haplotype analyses, are empowering us to generate staggering amounts of correlative data, from human/animal genetics and from normal and disease-affected tissues obtained from complex diseases such as arthritis. These tools are transforming molecular biology into a 'data rich' science, with subjects with an '-omic' suffix. These disciplines have to converge and integrate at a systemic level to examine the structure and dynamics of cellular and organismal function ('functionomics') simultaneously, using a multidimensional approach for cells, tissues, organs, rodents and Zebra fish models, which intertwines various approaches and readouts to study the development and homeostasis of a system. In summary, the postgenomic era of functionomics will facilitate narrowing the bridge between correlative data and causative data, thus integrating 'intercoms' of interacting and interdependent disciplines and forming a unified whole.

Bioinformatics: from genome to drug targets

The complete sequence determination of the human genome marks the start of a new era in biological science, with focus shifting from sequencing to functional mechanisms of gene products. In addition to effects on gene expression, most of the currently used therapeutic drugs either have enzymes or membrane proteins as their molecular targets of action. These membrane proteins include ion channels and transporters of small molecules, and receptors that convey signals from one side of a membrane to the other. Membrane proteins are thus involved in a variety of cellular processes and have a large potential as targets for new drug discovery. However, detailed structural information is still lacking for the majority of membrane proteins since their association with membrane constituents make NMR (nuclear magnetic resonance) spectroscopic and X-ray diffraction determinations difficult. Molecular modelling by biocomputing is a methodological alternative for structural studies of membrane proteins, but has to be based on experimental structural information in addition to computational techniques. A combination of bioinformatics and experimental techniques was used to model membrane proteins from two different classes, secondary transporters of the sodium:neurotransmitter symporter family (SNF transporters), and G-protein coupled receptors (GPCRs). The protein models were used to examine ligand-protein interactions and signalling/transport mechanisms, and to design experimental site-directed mutagenesis studies. Such studies have provided new insight into the detailed molecular mechanisms of two important classes of membrane proteins, which may be of value in the discovery and development of new pharmaceuticals.