Genome informatics. International Conference on Genome Informatics最新文献

Motivation: Methods like FBA and kinetic modeling are widely used to calculate fluxes in metabolic networks. For the analysis and understanding of simulation results and experimentally measured fluxes visualization software within the network context is indispensable.

Results: We present Flux Viz, an open-source Cytoscape plug-in for the visualization of flux distributions in molecular interaction networks. FluxViz supports (i) import of networks in a variety of formats (SBML, GML, XGMML, SIF, BioPAX, PSI-MI) (ii) import of flux distributions as CSV, Cytoscape attributes or VAL files (iii) limitation of views to flux carrying reactions (flux subnetwork) or network attributes like localization (iv) export of generated views (SVG, EPS, PDF, BMP, PNG). Though FluxViz was primarily developed as tool for the visualization of fluxes in metabolic networks and the analysis of simulation results from FASIMU, a flexible software for batch flux-balance computation in large metabolic networks, it is not limited to biochemical reaction networks and FBA but can be applied to the visualization of arbitrary fluxes in arbitrary graphs.

Availability: The platform-independent program is an open-source project, freely available at http://sourceforge.net/projects/fluxvizplugin/ under GNU public license, including manual, tutorial and examples.

Lipid mediator is the collective term for prostanoids, leukotrienes, lysophospholipids, platelet-activating factor, endocannabinoids and other bioactive lipids, that are involved in various physiological functions including inflammation, immune regulation and cellular development. They act by binding to their ligand-specific G-protein coupled receptors (GPCRs). Since 1990's a number of lipid GPCRs have been cloned in humans, with a few more identified in other vertebrates. However, the conservation of these receptors has been poorly investigated in other eukaryotes. Herein we performed a phylogenetic analysis by collecting their orthologs in 13 eukaryotes with complete genomes. The analysis shows that orthologs for prostanoid receptors are likely to be conserved in the 13 eukaryotes. In contrast, those for lysophospholipid and cannabinoid receptors appear to be conserved only in vertebrates and chordates. Receptors for leukotrienes and other bioactive lipids are limited to vertebrates. These results indicate that the lipid mediators and their receptors have coevolved with the development of highly modulated physiological functions such as immune regulation and the formation of the central nervous system. Accordingly, examining the presence and role of lipid mediator GPCR orthologs in invertebrate species can provide insight into the development of fundamental biological processes across diverse taxa.

Understanding the evolution and dynamics of metabolism in microbial ecosystems is an ongoing challenge in microbiology. A promising approach towards this goal is the extension of genome-scale flux balance models of metabolism to multiple interacting species. However, since the detailed distribution of metabolic functions among ecosystem members is often unknown, it is important to investigate how compartmentalization of metabolites and reactions affects flux balance predictions. Here, as a first step in this direction, we address the importance of compartmentalization in the well characterized metabolic model of the yeast Saccharomyces cerevisiae, which we treat as an "ecosystem of organelles". In addition to addressing the impact that the removal of compartmentalization has on model predictions, we show that by systematically constraining some individual fluxes in a de-compartmentalized version of the model we can significantly reduce the flux prediction errors induced by the removal of compartments. We expect that our analysis will help predict and understand metabolic functions in complex microbial communities. In addition, further study of yeast as an ecosystem of organelles might provide novel insight on the evolution of endosymbiosis and multicellularity.

While the importance of modulatory proteolysis in research has steadily increased, knowledge on this process has remained largely disorganized, with the nature and role of entities composing modulatory proteolysis still uncertain. We built CaMPDB, a resource on modulatory proteolysis, with a focus on calpain, a well-studied intracellular protease which regulates substrate functions by proteolytic processing. CaMPDB contains sequences of calpains, substrates and inhibitors as well as substrate cleavage sites, collected from the literature. Some cleavage efficiencies were evaluated by biochemical experiments and a cleavage site prediction tool is provided to assist biologists in understanding calpain-mediated cellular processes. CaMPDB is freely accessible at http://calpain.org.

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

Coexpressed genes are tentatively translated into proteins that are involved in similar biological functions. Here, we constructed gene coexpression networks from collected microarray data of the organisms Arabidopsis thaliana, Saccharomyces cerevisiae, and Escherichia coli. Their degree distributions show the common property of an overrepresentation of highly connected nodes followed by a sudden truncation. In order to analyze this behavior, we present an evolutionary model simulating the genetic evolution. This model assumes that new genes emerge by duplication from a small initial set of primordial genes. Our model does not include the removal of unused genes but selective pressure is indirectly taken into account by preferentially duplicating the old genes. Thus, gene duplication represents the emergence of a new gene and its successful establishment. After a duplication event, all genes are slightly but iteratively mutated, thus altering their expression patterns. Our model is capable of reproducing global properties of the investigated coexpression networks. We show that our model reflects the mean inter-node distances and especially the characteristic humps in the degree distribution that, in the biological examples, result from functionally related genes.

Many cofactors and nucleotides containing sulfur atoms are known to have important functions in a variety of organisms. Recently, the biosynthetic pathways of these sulfur containing compounds have been revealed, where many enzymes relay sulfur atoms. Increasing evidence also suggests that the prokaryotic sulfur-relay enzymes might be the evolutionary origin of ubiquitination and the related systems that control a wide range of physiological processes in eukaryotic cells. However, these sulfur-relay enzymes have been studied in only a small number of organisms. Here we carried out comparative genomic analysis and examined the presence and absence of sulfurtransferases utilized in the biosynthetic pathways of molybdenum cofactor (Moco), 2-thiouridine (S(2)U), and 4-thiouridine (S(4)U), and IscS, a cysteine desulfurase. We found that all eukaryotes and many other organisms lack the intermediate enzymes in S(2)U biosynthesis. It is also found that most genes lack rhodanese homology domain (RHD), a catalytic domain of sulfurtransferase. Some organisms have a conserved sequence composed of about 100 residues in the C terminus of TusA, different from RHD. Host-associated organisms have a tendency to lose Moco biosynthetic enzymes, and some organisms have MoaD-MoaE fusion protein. Our findings suggest that sulfur-relay pathways have been so diversified that some putative sulfurtransferases possibly function in other unknown pathways.

The high osmolarity glycerol (HOG) signalling system in yeast belongs to the class of Mitogen Activated Protein Kinase (MAPK) pathways that are found in all eukaryotic organisms. It includes at least three scaffold proteins that form complexes, and involves reactions that are strictly dependent on the set of species bound to a certain complex. The scaffold proteins lead to a combinatorial increase in the number of possible states. To date, representations of the HOG pathway have used simplifying assumptions to avoid this combinatorial problem. Such assumptions are hard to make and may obscure or remove essential properties of the system. This paper presents a detailed generic formal representation of the HOG system without such assumptions, showing the molecular interactions known from the literature. The model takes complexes into account, and summarises existing knowledge in an unambiguous and detailed representation. It can thus be used to anchor discussions about the HOG system. In the commonly used Systems Biology Markup Language (SBML), such a model would need to explicitly enumerate all state variables. The Kappa modelling language which we use supports representation of complexes without such enumeration. To conclude, we compare Kappa with a few other modelling languages and software tools that could also be used to represent and model the HOG system.