Streptomyces coelicolor is a representative of the group of soil-dwelling, filamentous bacteria responsible for producing most natural antibiotics used in human and veterinary medicine. Here we report the 8,667,507 base pair linear chromosome of this organism, containing the largest number of genes so far discovered in a bacterium. The 7,825 predicted genes include more than 20 clusters coding for known or predicted secondary metabolites. The genome contains an unprecedented proportion of regulatory genes, predominantly those likely to be involved in responses to external stimuli and stresses, and many duplicated gene sets that may represent 'tissue-specific' isoforms operating in different phases of colonial development, a unique situation for a bacterium. An ancient synteny was revealed between the central 'core' of the chromosome and the whole chromosome of pathogens Mycobacterium tuberculosis and Corynebacterium diphtheriae. The genome sequence will greatly increase our understanding of microbial life in the soil as well as aiding the generation of new drug candidates by genetic engineering.

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Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2)

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.

/pdf/the-metacyc-database-of-metabolic-pathways-and-enzymes-and-4y3a7lo51l.pdf

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Motivation: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program.

Results: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions.

Availability: http://www.psort.org/psortb (download open source software or use the web interface).

Contact: psort-mail@sfu.ca

Supplementary Information:Supplementary data are availableat Bioinformatics online.

PSORTb 3.0

Biofilms contain bacterial cells that are in a wide range of physiological states. Within a biofilm population, cells with diverse genotypes and phenotypes that express distinct metabolic pathways, stress responses and other specific biological activities are juxtaposed. The mechanisms that contribute to this genetic and physiological heterogeneity include microscale chemical gradients, adaptation to local environmental conditions, stochastic gene expression and the genotypic variation that occurs through mutation and selection. Here, we discuss the processes that generate chemical gradients in biofilms, the genetic and physiological responses of the bacteria as they adapt to these gradients and the techniques that can be used to visualize and measure the microscale physiological heterogeneities of bacteria in biofilms.

Physiological heterogeneity in biofilms.

This comprehensive State of the Art review summarizes the current published knowledge base regarding the pathophysiology and microbiology of pulmonary disease in cystic fibrosis (CF). The molecular basis of CF lung disease including the impact of defective cystic fibrosis transmembrane regulator (CFTR) protein function on airway physiology, mucociliary clearance, and establishment of Pseudomonas aeruginosa infection is described. An extensive review of the microbiology of CF lung disease with particular reference to infection with P. aeruginosa is provided. Other pathogens commonly associated with CF lung disease including Staphylococcal aureus, Burkholderia cepacia, Stenotrophomonas maltophilia, Achromobacter xylosoxidans and atypical mycobacteria are also described. Clinical presentation and assessment of CF lung disease including diagnostic microbiology and other measures of pulmonary health are reviewed. Current recommendations for management of CF lung disease are provided. An extensive review of antipseudomonal therapies in the settings of treatment for early P. aeruginosa infection, maintenance for patients with chronic P. aeruginosa infection, and treatment of exacerbation in pulmonary symptoms, as well as antibiotic therapies for other CF respiratory pathogens, are included. In addition, the article discusses infection control policies, therapies to optimize airway clearance and reduce inflammation, and potential future therapies.

Pathophysiology and management of pulmonary infections in cystic fibrosis.

Pseudomonas aeruginosa is a ubiquitous environmental bacterium that is one of the top three causes of opportunistic human infections. A major factor in its prominence as a pathogen is its intrinsic resistance to antibiotics and disinfectants. Here we report the complete sequence of P. aeruginosa strain PAO1. At 6.3 million base pairs, this is the largest bacterial genome sequenced, and the sequence provides insights into the basis of the versatility and intrinsic drug resistance of P. aeruginosa. Consistent with its larger genome size and environmental adaptability, P. aeruginosa contains the highest proportion of regulatory genes observed for a bacterial genome and a large number of genes involved in the catabolism, transport and efflux of organic compounds as well as four potential chemotaxis systems. We propose that the size and complexity of the P. aeruginosa genome reflect an evolutionary adaptation permitting it to thrive in diverse environments and resist the effects of a variety of antimicrobial substances.

/pdf/complete-genome-sequence-of-pseudomonas-aeruginosa-pao1-an-u88d4e2n3p.pdf

L. Goltry

Papers

Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen.

Antimicrobial effects of novel siderophores linked to β-lactam antibiotics