Alice R. Wattam

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

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 Issue.

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species

ABSTRACT Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRICs outreach activities, collaborative endeavors, and future research directions is provided.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by ‘virtual integration’ to any of PATRICs public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

Antimicrobial Resistance Prediction in PATRIC and RAST

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88–99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71–88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.

Comparative Genomics of Early-Diverging Brucella Strains Reveals a Novel Lipopolysaccharide Biosynthesis Pathway

ABSTRACT Brucella species are Gram-negative bacteria that infect mammals. Recently, two unusual strains (Brucella inopinata BO1T and B. inopinata-like BO2) have been isolated from human patients, and their similarity to some atypical brucellae isolated from Australian native rodent species was noted. Here we present a phylogenomic analysis of the draft genome sequences of BO1T and BO2 and of the Australian rodent strains 83-13 and NF2653 that shows that they form two groups well separated from the other sequenced Brucella spp. Several important differences were noted. Both BO1T and BO2 did not agglutinate significantly when live or inactivated cells were exposed to monospecific A and M antisera against O-side chain sugars composed of N-formyl-perosamine. While BO1T maintained the genes required to synthesize a typical Brucella O-antigen, BO2 lacked many of these genes but still produced a smooth LPS (lipopolysaccharide). Most missing genes were found in the wbk region involved in O-antigen synthesis in classic smooth Brucella spp. In their place, BO2 carries four genes that other bacteria use for making a rhamnose-based O-antigen. Electrophoretic, immunoblot, and chemical analyses showed that BO2 carries an antigenically different O-antigen made of repeating hexose-rich oligosaccharide units that made the LPS water-soluble, which contrasts with the homopolymeric O-antigen of other smooth brucellae that have a phenol-soluble LPS. The results demonstrate the existence of a group of early-diverging brucellae with traits that depart significantly from those of the Brucella species described thus far. IMPORTANCE This report examines differences between genomes from four new Brucella strains and those from the classic Brucella spp. Our results show that the four new strains are outliers with respect to the previously known Brucella strains and yet are part of the genus, forming two new clades. The analysis revealed important information about the evolution and survival mechanisms of Brucella species, helping reshape our knowledge of this important zoonotic pathogen. One discovery of special importance is that one of the strains, BO2, produces an O-antigen distinct from any that has been seen in any other Brucella isolates to date. This report examines differences between genomes from four new Brucella strains and those from the classic Brucella spp. Our results show that the four new strains are outliers with respect to the previously known Brucella strains and yet are part of the genus, forming two new clades. The analysis revealed important information about the evolution and survival mechanisms of Brucella species, helping reshape our knowledge of this important zoonotic pathogen. One discovery of special importance is that one of the strains, BO2, produces an O-antigen distinct from any that has been seen in any other Brucella isolates to date.

Peptide nucleic acids inhibit growth of Brucella suis in pure culture and in infected murine macrophages.

Peptide nucleic acids (PNAs) are single-stranded, synthetic nucleic acid analogues containing a pseudopeptide backbone in place of the phosphodiester sugar-phosphate. When PNAs are covalently linked to cell-penetrating peptides (CPPs) they readily penetrate the bacterial cell envelope, inhibit expression of targeted genes and cause growth inhibition both of Gram-positive and Gram-negative bacteria. However, the effectiveness of PNAs against Brucella, a facultative intracellular bacterial pathogen, was unknown. The susceptibility of a virulent Brucella suis strain to a variety of PNAs was assessed in pure culture as well as in murine macrophages. The studies showed that some of the PNAs targeted to Brucella genes involved in DNA (polA, dnaG, gyrA), RNA (rpoB), cell envelope (asd), fatty acid (kdtA, acpP) and protein (tsf) synthesis inhibit the growth of B. suis in culture and in macrophages after 24 h of treatment. PNA treatment inhibited Brucella growth by interfering with gene expression in a sequence-specific and dose-dependent manner at micromolar concentrations. The most effective PNA in broth culture was that targeting polA at ca. 12 μM. In contrast, in B. suis-infected macrophages, the most effective PNAs were those targeting asd and dnaG at 30 μM; both of these PNAs had little inhibitory effect on Brucella in broth culture. The polA PNA that inhibits wild-type B. suis also inhibits the growth of wild-type Brucella melitensis 16M and Brucella abortus 2308 in culture. This study reveals the potential usefulness of antisense PNA constructs as novel therapeutic agents against intracellular Brucella.

Curation, integration and visualization of bacterial virulence factors in PATRIC

Motivation: We’ve developed a highly curated bacterial virulence factor (VF) library in PATRIC (Pathosystems Resource Integration Center, www.patricbrc.org) to support infectious disease research. Although several VF databases are available, there is still a need to incorporate new knowledge found in published experimental evidence and integrate these data with other information known for these specific VF genes, including genomic and other omics data. This integration supports the identification of VFs, comparative studies and hypothesis generation, which facilitates the understanding of virulence and pathogenicity. Results: We have manually curated VFs from six prioritized NIAID (National Institute of Allergy and Infectious Diseases) category A–C bacterial pathogen genera, Mycobacterium, Salmonella, Escherichia, Shigella, Listeria and Bartonella, using published literature. This curated information on virulence has been integrated with data from genomic functional annotations, trancriptomic experiments, protein–protein interactions and disease information already present in PATRIC. Such integration gives researchers access to a broad array of information about these individual genes, and also to a suite of tools to perform comparative genomic and transcriptomics analysis that are available at PATRIC. Availability and implementation: All tools and data are freely available at PATRIC (http://patricbrc.org). Contact: cmao@vbi.vt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Transcriptome-Wide Identification of Hfq-Associated RNAs in Brucella suis by Deep Sequencing

UNLABELLED Recent breakthroughs in next-generation sequencing technologies have led to the identification of small noncoding RNAs (sRNAs) as a new important class of regulatory molecules. In prokaryotes, sRNAs are often bound to the chaperone protein Hfq, which allows them to interact with their partner mRNA(s). We screened the genome of the zoonotic and human pathogen Brucella suis 1330 for the presence of this class of RNAs. We designed a coimmunoprecipitation strategy that relies on the use of Hfq as a bait to enrich the sample with sRNAs and eventually their target mRNAs. By deep sequencing analysis of the Hfq-bound transcripts, we identified a number of mRNAs and 33 sRNA candidates associated with Hfq. The expression of 10 sRNAs in the early stationary growth phase was experimentally confirmed by Northern blotting and/or reverse transcriptase PCR. IMPORTANCE Brucella organisms are facultative intracellular pathogens that use stealth strategies to avoid host defenses. Adaptation to the host environment requires tight control of gene expression. Recently, small noncoding RNAs (sRNAs) and the sRNA chaperone Hfq have been shown to play a role in the fine-tuning of gene expression. Here we have used RNA sequencing to identify RNAs associated with the B. suis Hfq protein. We have identified a novel list of 33 sRNAs and 62 Hfq-associated mRNAs for future studies aiming to understand the intracellular lifestyle of this pathogen.