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Hematopoietic growth factor inducible neurokinin-1 type: a transmembrane protein that is similar to neurokinin 1 interacts with substance P.
Neurokinin 1 (NK-1) is a member of seven transmembrane G protein-coupled receptors. NK-1 interacts with peptides belonging to the tachykinin family and showed preference for substance P (SP). NK-1 is induced in bone marrow (BM) stroma. NK-1-SP interactions could lead to changes in the functions of lymphohematopoietic stem cell (LHSC). This report describes the cloning and characterization of a cDNA clone isolated after screening of three cDNA libraries with an NK-1-specific probe. Based on its expression, the cDNA clone was designated hematopoietic growth factor inducible neurokinin-1 type (HGFIN). Computational analyses predicted that HGFIN is transmembrane with the carboxyl terminal extracellular. Proteomic studies with purified HGFIN and SP showed noncovalent interactions. HGFIN-SP interactions were supported by transient expression of HGFIN in CHO cells. Transient expression of HGFIN in unstimulated BM fibroblasts led to the induction of endogenous NK-1. Since NK-1 expression in BM fibroblasts requires cell stimulation, these studies suggest that there might be intracellular crosstalk between NK-1 and HGFIN. Northern analyses with total RNA from different BM cell subsets showed that HGFIN was preferentially expressed in differentiated cells. This suggests that HGFIN might be involved in the maturation of LHSC. HGFIN was detected in several other tissues, but not in brain where NK-1 is constitutively expressed.
Determination of biomass composition of Catharanthus roseus hairy roots for metabolic flux analysis.
Metabolic flux analysis is a powerful diagnostic tool in metabolic engineering, and determination of biomass composition is indispensable to accurate flux evaluation. We report the elemental and biomolecular composition of Catharanthus roseus hairy roots, a pharmaceutically significant plant system and an important metabolic engineering target. The molecular formula of the organic material in the hairy roots was C12.0H22.7N0.4O7.6 during mid-exponential growth. The abundances of lipids, lignin, cellulose, hemicellulose, starch, protein, proteinogenic amino acids, mineral ash, and moisture in the biomass were quantified. Analysis of water-soluble components of the biomass with 1-D 13C and 2-D [1H,1H] correlation (COSY) NMR spectroscopy revealed that the water-soluble components were composed almost entirely of -glucans. Agropine, a frequently reported hairy root biomass component, was not detected. Our measurements of the biomass components quantified 83.6 +/- 9.3% (w/w) of the biomass. Together with previously reported abundances of indole alkaloids, we accounted for at least 85.9 +/- 11.6% (w/w) of the carbon in the biomass, which enabled the precise determination of 12 biomass synthesis fluxes.
Effect of sampling on topology predictions of protein-protein interaction networks.
Currently available protein-protein interaction (PPI) network or 'interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies--most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing interactome maps cannot be confidently extrapolated to complete interactomes.
The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.
A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).
Summation theorems for flux and concentration control coefficients of dynamic systems.
Metabolic control analysis (MCA) was developed to quantify how system variables are affected by parameter variations in a system. In addition, MCA can express the global properties of a system in terms of the individual catalytic steps, using connectivity and summation theorems to link the control coefficients to the elasticity coefficients. MCA was originally developed for steady-state analysis and not all summation theorems have been derived for dynamic systems. A method to determine time-dependent flux and concentration control coefficients for dynamic systems by expressing the time domain as a function of percentage progression through any arbitrary fixed interval of time is reported. Time-dependent flux and concentration control coefficients of dynamic systems, provided that they are evaluated in this novel way, obey the same summation theorems as steady-state flux and concentration control coefficients, respectively.
Expansion of signal transduction networks.
Signal transduction networks of different cell types show a large variety in their structural design. In this paper, basic structural properties of signal transduction networks are investigated. For this, such networks with the recently developed method of network expansion are analysed. This method allows for a structural analysis of networks by calculating signal expansion profiles when provided with certain compounds, for example growth factors, inactive kinases and so on (seed compounds), to initiate such an expansion. The presented results may put forth valuable hints on the evolution of signalling networks.
Bioinformatics: biology by other means.
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The Pathway Tools software.
MOTIVATION: Bioinformatics requires reusable software tools for creating model-organism databases (MODs). RESULTS: The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.
The connectivity structure, giant strong component and centrality of metabolic networks.
MOTIVATION: Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. RESULTS: In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/
MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis.
SUMMARY: MetaFluxNet is a program package for managing information on the metabolic reaction network and for quantitatively analyzing metabolic fluxes in an interactive and customized way. It allows users to interpret and examine metabolic behavior in response to genetic and/or environmental modifications. As a result, quantitative in silico simulations of metabolic pathways can be carried out to understand the metabolic status and to design the metabolic engineering strategies. The main features of the program include a well-developed model construction environment, user-friendly interface for metabolic flux analysis (MFA), comparative MFA of strains having different genotypes under various environmental conditions, and automated pathway layout creation. AVAILABILITY: http://mbel.kaist.ac.kr/ SUPPLEMENTARY INFORMATION: A manual for MetaFluxNet is available as PDF file.
MedlineR: an open source library in R for Medline literature data mining.
SUMMARY: We describe an open source library written in the R programming language for Medline literature data mining. This MedlineR library includes programs to query Medline through the NCBI PubMed database; to construct the co-occurrence matrix; and to visualize the network topology of query terms. The open source nature of this library allows users to extend it freely in the statistical programming language of R. To demonstrate its utility, we have built an application to analyze term-association by using only 10 lines of code. We provide MedlineR as a library foundation for bioinformaticians and statisticians to build more sophisticated literature data mining applications. AVAILABILITY: The library is available from http://dbsr.duke.edu/pub/MedlineR.
Knowledge-based framework for hypothesis formation in biochemical networks.
MOTIVATION: The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding 'pattern' in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalisms they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about biochemical networks. RESULTS: We present a knowledge-based framework for hypothesis formation for biochemical networks. The framework has been implemented by extending BioSigNet-RR-a knowledge based system that supports elaboration-tolerant representation and non-monotonic reasoning. Features of the extended system are illustrated by a case study of the p53 signal network. AVAILABILITY: http://www.biosignet.org
Integrative data analysis for functional prediction: a multi-objective optimization approach.
UNLABELLED: An integrative classification system for functional genomics is introduced. A comparison with a previous study of the yeast mitochondrial proteome is presented. AVAILABILITY: A demonstration prototype, interSearch, is available on request. SUPPLEMENTARY INFORMATION: http://ijsr32.infj.ulster.ac.uk/~e10110731/interSearch.
The UCSC Known Genes.
The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were constructed. For each known gene, a details page is provided containing rich information about the gene, together with extensive links to other relevant genomic, proteomic and pathway data. As of July 2005, the UCSC Known Genes are available for human, mouse and rat genomes. The Known Genes serves as a foundation to support several key programs: the Genome Browser, Proteome Browser, Gene Sorter and Table Browser offered at the UCSC website. All the associated data files and program source code are also available. They can be accessed at http://genome.ucsc.edu. The genomic coverage of UCSC Known Genes, RefSeq, Ensembl Genes, H-Invitational and CCDS is analyzed. Although UCSC Known Genes offers the highest genomic and CDS coverage among major human and mouse gene sets, more detailed analysis suggests all of them could be further improved.
PepSeeker: a database of proteome peptide identifications for investigating fragmentation patterns.
Proteome science relies on bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. The resulting secondary ion mass spectra are compared with protein sequence databases in order to identify the amino acid sequence. Although these search tools (e.g. SEQUEST, Mascot, X!Tandem, Phenyx) are frequently successful, much is still not understood about the amino acid sequence patterns which promote/protect particular fragmentation pathways, and hence lead to the presence/absence of particular ions from different ion series. In order to advance this area, we have developed a database, PepSeeker (http://nwsr.smith.man.ac.uk/pepseeker), which captures this peptide identification and ion information from proteome experiments. The database currently contains >185,000 peptides and associated database search information. Users may query this resource to retrieve peptide, protein and spectral information based on protein or peptide information, including the amino acid sequence itself represented by regular expressions coupled with ion series information. We believe this database will be useful to proteome researchers wishing to understand gas phase peptide ion chemistry in order to improve peptide identification strategies. Questions can be addressed to j.selley@manchester.ac.uk.
MitoP2: the mitochondrial proteome database--now including mouse data.
The MitoP2 database (http://www.mitop.de) integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria.