PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issu

Genomic analysis of pasteurella atlantica provides insight on its virulence factors and phylogeny and highlights the potential of reverse vaccinology in aquaculture

Pasteurellosis in farmed lumpsuckers, Cyclopterus lumpus, has emerged as a serious disease in Norwegian aquaculture in recent years. Genomic characterization of the causative agent is essential in understanding the biology of the bacteria involved and in devising an efficient preventive strategy. The genomes of two clinical Pasteurella atlantica isolates were sequenced (≈2.3 Mbp), and phylogenetic analysis confirmed their position as a novel species within the Pasteurellaceae. In silico analyses revealed 11 genomic islands and 5 prophages, highlighting the potential of mobile elements as driving forces in the evolution of this species. The previously documented pathogenicity of P. atlantica is strongly supported by the current study, and 17 target genes were recognized as putative primary drivers of pathogenicity. The expression level of a predicted vaccine target, an uncharacterized adhesin protein, was significantly increased in both broth culture and following the exposure of P. atlantica to lumpsucker head kidney leucocytes. Based on in silico and functional analyses, the strongest gene target candidates will be prioritized in future vaccine development efforts to prevent future pasteurellosis outbreaks.publishedVersio

Brucellosis Ontology (IDOBRU) as an extension of the Infectious Disease Ontology

<p>Abstract</p> <p>Background</p> <p>Caused by intracellular Gram-negative bacteria <it>Brucella </it>spp., brucellosis is the most common bacterial zoonotic disease. Extensive studies in brucellosis have yielded a large number of publications and data covering various topics ranging from basic <it>Brucella </it>genetic study to vaccine clinical trials. To support data interoperability and reasoning, a community-based brucellosis-specific biomedical ontology is needed.</p> <p>Results</p> <p>The Brucellosis Ontology (IDOBRU: <url>http://sourceforge.net/projects/idobru</url>), a biomedical ontology in the brucellosis domain, is an extension ontology of the core Infectious Disease Ontology (IDO-core) and follows OBO Foundry principles. Currently IDOBRU contains 1503 ontology terms, which includes 739 <it>Brucella</it>-specific terms, 414 IDO-core terms, and 350 terms imported from 10 existing ontologies. IDOBRU has been used to model different aspects of brucellosis, including host infection, zoonotic disease transmission, symptoms, virulence factors and pathogenesis, diagnosis, intentional release, vaccine prevention, and treatment. Case studies are typically used in our IDOBRU modeling. For example, diurnal temperature variation in <it>Brucella </it>patients, a <it>Brucella</it>-specific PCR method, and a WHO-recommended brucellosis treatment were selected as use cases to model brucellosis symptom, diagnosis, and treatment, respectively. Developed using OWL, IDOBRU supports OWL-based ontological reasoning. For example, by performing a Description Logic (DL) query in the OWL editor Protégé 4 or a SPARQL query in an IDOBRU SPARQL server, a check of <it>Brucella </it>virulence factors showed that eight of them are known protective antigens based on the biological knowledge captured within the ontology.</p> <p>Conclusions</p> <p>IDOBRU is the first reported bacterial infectious disease ontology developed to represent different disease aspects in a formal logical format. It serves as a brucellosis knowledgebase and supports brucellosis data integration and automated reasoning.</p

MOCAT2: a metagenomic assembly, annotation and profiling framework

MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. Availability and Implementation: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de. Contact: [email protected]

Genome analysis of a new Rhodothermaceae strain isolated from a hot spring

A bacterial strain, designated RA, was isolated from water sample of a hot spring on Langkawi Island of Malaysia using marine agar. Strain RA is an aerophilic and thermophilic microorganism that grows optimally at 50-60°C and is capable of growing in marine broth containing 1-10% (w/v) NaCl. 16S rRNA gene sequence analysis demonstrated that this strain is most closely related (<90% sequence identity) to Rhodothermaceae, which currently comprises of six genera: Rhodothermus (two species), Salinibacter (three species), Salisaeta (one species), Rubricoccus (one species), Rubrivirga (one species), and Longimonas (one species). Notably, analysis of average nucleotide identity (ANI) values indicated that strain RA may represent the first member of a novel genus of Rhodothermaceae. The draft genome of strain RA is 4,616,094 bp with 3630 protein-coding gene sequences. Its GC content is 68.3%, which is higher than that of most other genomes of Rhodothermaceae. Strain RA has genes for sulfate permease and arylsulfatase to withstand the high sulfur and sulfate contents of the hot spring. Putative genes encoding proteins involved in adaptation to osmotic stress were identified which encode proteins namely Na+/H+ antiporters, a sodium/solute symporter, a sodium/glutamate symporter, trehalose synthase, malto-oligosyltrehalose synthase, choline-sulfatase, potassium uptake proteins (TrkA and TrkH), osmotically inducible protein C, and the K+ channel histidine kinase KdpD. Furthermore, genome description of strain RA and comparative genome studies in relation to other related genera provide an overview of the uniqueness of this bacterium

Genomic Analysis of Metabolic Differences Found in \u3ci\u3eClostridium perfringens\u3c/i\u3e That Cause Necrotic Enteritis in Poultry

Clostridium perfringens is a common member of gut microbiota in healthy animals, but can also be an important pathogen in human and veterinary medicine. It produces several protein toxins that contribute to both histotoxic and enteric diseases in animals. Necrotic enteritis in poultry has been associated with the NetB toxin of C. perfringens; however, this toxin alone is insufficient to cause disease in infected chickens. While considerable research has focused on the presence of toxins and virulence factors, little has been done to assess the function of metabolic factors on the ability of the bacteria to cause disease. In this study, the metabolic differences are examined using genomic sequence analysis between genomes of stains of C. perfringens that are associated with necrotic enteritis. Several metabolic pathways are examined, which show different metabolic genes across C. perfringens lineages. Advisors: Greg Somerville and Etsuko Moriyam

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community thereby enabling them to design novel biomedicine against such infectious diseases