Towards the proteome of the marine bacterium Rhodopirellula baltica: mapping the soluble proteins

The marine bacterium Rhodopirellula baltica, a member of the phylum Planctomycetes, has distinct morphological properties and contributes to remineralization of biomass in the natural environment. On the basis of its recently determined complete genome we investigated its proteome by 2-DE and established a reference 2-DE gel for the soluble protein fraction. Approximately 1000 protein spots were excised from a colloidal Coomassie-stained gel (pH 4-7), analyzed by MALDI-MS and identified by PMF. The non-redundant data set contained 626 distinct protein spots, corresponding to 558 different genes. The identified proteins were classified into role categories according to their predicted functions. The experimentally determined and the theoretically predicted proteomes were compared. Proteins, which were most abundant in 2-DE gels and the coding genes of which were also predicted to be highly expressed, could be linked mainly to housekeeping functions in glycolysis, tricarboxic acid cycle, amino acid biosynthesis, protein quality control and translation. Absence of predictable signal peptides indicated a localization of these proteins in the intracellular compartment, the pirellulosome. Among the identified proteins, 146 contained a predicted signal peptide suggesting their translocation. Some proteins were detected in more than one spot on the gel, indicating post-translational modification. In addition to identifying proteins present in the published sequence database for R. baltica, an alternative approach was used, in which the mass spectrometric data was searched against a maximal ORF set, allowing the identification of four previously unpredicted ORFs. The 2-DE reference map presented here will serve as framework for further experiments to study differential gene expression of R. baltica in response to external stimuli or cellular development and compartmentalization

MetaMine – A tool to detect and analyse gene patterns in their environmental context

Background Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes. Results MetaMine is an interactive data mining tool which enables the detection of gene patterns in an environmental context. The gene pattern search starts with a user defined environmentally interesting key gene. With this gene a BLAST search is carried out against the Microbial Ecological Genomics DataBase (MEGDB) containing marine genomic and metagenomic sequences. This is followed by the determination of all neighbouring genes within a given distance and a search for functionally equivalent genes. In the final step a set of common genes present in a defined number of distinct genomes is determined. The gene patterns found are associated with their individual pattern instances describing gene order and directions. They are presented together with information about the sample and the habitat. MetaMine is implemented in Java and provided as a client/server application with a user-friendly graphical user interface. The system was evaluated with environmentally relevant genes related to the methane-cycle and carbon monoxide oxidation. Conclusion MetaMine offers a targeted, semi-automatic search for gene patterns based on expert input. The graphical user interface of MetaMine provides a user-friendly overview of the computed gene patterns for further inspection in an ecological context. Prevailing biological processes associated with a key gene can be used to infer new annotations and shape hypotheses to guide further analyses. The use-cases demonstrate that meaningful gene patterns can be quickly detected using MetaMine

Updates in Rhea-a manually curated resource of biochemical reactions.

Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive and non-redundant resource of expert-curated biochemical reactions described using species from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Rhea has been designed for the functional annotation of enzymes and the description of genome-scale metabolic networks, providing stoichiometrically balanced enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list and additional reactions), transport reactions and spontaneously occurring reactions. Rhea reactions are extensively curated with links to source literature and are mapped to other publicly available enzyme and pathway databases such as Reactome, BioCyc, KEGG and UniPathway, through manual curation and computational methods. Here we describe developments in Rhea since our last report in the 2012 database issue of Nucleic Acids Research. These include significant growth in the number of Rhea reactions and the inclusion of reactions involving complex macromolecules such as proteins, nucleic acids and other polymers that lie outside the scope of ChEBI. Together these developments will significantly increase the utility of Rhea as a tool for the description, analysis and reconciliation of genome-scale metabolic models

Updates in Rhea - an expert curated resource of biochemical reactions.

Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of expert-curated biochemical reactions designed for the functional annotation of enzymes and the description of metabolic networks. Rhea describes enzyme-catalyzed reactions covering the IUBMB Enzyme Nomenclature list as well as additional reactions, including spontaneously occurring reactions, using entities from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Here we describe developments in Rhea since our last report in the database issue of Nucleic Acids Research. These include the first implementation of a simple hierarchical classification of reactions, improved coverage of the IUBMB Enzyme Nomenclature list and additional reactions through continuing expert curation, and the development of a new website to serve this improved dataset

Updates in Rhea: SPARQLing biochemical reaction data.

Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of over 11 000 expert-curated biochemical reactions that uses chemical entities from the ChEBI ontology to represent reaction participants. Originally designed as an annotation vocabulary for the UniProt Knowledgebase (UniProtKB), Rhea also provides reaction data for a range of other core knowledgebases and data repositories including ChEBI and MetaboLights. Here we describe recent developments in Rhea, focusing on a new resource description framework representation of Rhea reaction data and an SPARQL endpoint (https://sparql.rhea-db.org/sparql) that provides access to it. We demonstrate how federated queries that combine the Rhea SPARQL endpoint and other SPARQL endpoints such as that of UniProt can provide improved metabolite annotation and support integrative analyses that link the metabolome through the proteome to the transcriptome and genome. These developments will significantly boost the utility of Rhea as a means to link chemistry and biology for a more holistic understanding of biological systems and their function in health and disease

Adaptations to Submarine Hydrothermal Environments Exemplified by the Genome of Nautilia profundicola

Submarine hydrothermal vents are model systems for the Archaean Earth environment, and some sites maintain conditions that may have favored the formation and evolution of cellular life. Vents are typified by rapid fluctuations in temperature and redox potential that impose a strong selective pressure on resident microbial communities. Nautilia profundicola strain Am-H is a moderately thermophilic, deeply-branching Epsilonproteobacterium found free-living at hydrothermal vents and is a member of the microbial mass on the dorsal surface of vent polychaete, Alvinella pompejana. Analysis of the 1.7-Mbp genome of N. profundicola uncovered adaptations to the vent environment—some unique and some shared with other Epsilonproteobacterial genomes. The major findings included: (1) a diverse suite of hydrogenases coupled to a relatively simple electron transport chain, (2) numerous stress response systems, (3) a novel predicted nitrate assimilation pathway with hydroxylamine as a key intermediate, and (4) a gene (rgy) encoding the hallmark protein for hyperthermophilic growth, reverse gyrase. Additional experiments indicated that expression of rgy in strain Am-H was induced over 100-fold with a 20°C increase above the optimal growth temperature of this bacterium and that closely related rgy genes are present and expressed in bacterial communities residing in geographically distinct thermophilic environments. N. profundicola, therefore, is a model Epsilonproteobacterium that contains all the genes necessary for life in the extreme conditions widely believed to reflect those in the Archaean biosphere—anaerobic, sulfur, H2- and CO2-rich, with fluctuating redox potentials and temperatures. In addition, reverse gyrase appears to be an important and common adaptation for mesophiles and moderate thermophiles that inhabit ecological niches characterized by rapid and frequent temperature fluctuations and, as such, can no longer be considered a unique feature of hyperthermophiles

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits