The use of prescription medications and non-prescription medications during lactation in a prospective Canadian cohort study.

BACKGROUND: A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. METHODS: Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. RESULTS: A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. CONCLUSIONS: In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation.

Cohort profile: investigating SARS-CoV-2 infection and the health and psychosocial impact of the COVID-19 pandemic in the Canadian CHILD Cohort.

The coronavirus disease 2019 (COVID-19) pandemic has affected all Canadian families, with some impacted differently than others. Our study aims to: (1) determine the prevalence and transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among Canadian families, (2) identify predictors of infection susceptibility and severity of SARS-CoV-2, and (3) identify health and psychosocial impacts of the COVID-19 pandemic. This study builds upon the CHILD Cohort Study, an ongoing multi-ethnic general population prospective cohort consisting of 3,454 Canadian families with children born in Vancouver, Edmonton, Manitoba, and Toronto between 2009 and 2012. During the pandemic, CHILD households were invited to participate in the CHILD COVID-19 Add-On Study involving: (1) brief biweekly surveys about COVID-19 symptoms and testing; (2) quarterly questionnaires assessing COVID-19 exposure and testing, vaccination status, physical and mental health, and pandemic-driven life changes; and (3) in-home biological sampling kits to collect blood and stool. In total, 1,462 households (5,378 participants) consented to the CHILD COVID-19 Add-On Study: 2,803 children (mean±standard deviation [SD], 9.0±2.7 years; range, 0-17 years) and 2,576 adults (mean±SD, 43.0±6.5 years; range, 18-85 years). We will leverage the wealth of pre-pandemic CHILD data to identify risk and resilience factors for susceptibility and severity to the direct and indirect pandemic effects. Our short-term findings will inform key stakeholders and knowledge users to shape current and future pandemic responses. Additionally, this study provides a unique resource to study the long-term impacts of the pandemic as the CHILD Cohort Study continues.

GlobeCorr: interactive globe-based visualization for correlation datasets.

Motivation: Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations; however, they lack secure, dynamic visualization capability. Results: GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualization and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualization using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user's computer) and provides a simple method for users to visualize and summarize complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. Availability and Implementation: See https://GlobeCorr.ca; Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr.

From Prescription Drugs to Natural Health Products: Medication Use in Canadian Infants.

Limited data exist on pharmaceutical product use by infants, although available data suggests higher prevalence of use among children under 12 months of age. We conducted a descriptive study of 3050 infants recruited in the CHILD Cohort Study, a prospective, multicenter, longitudinal cohort following children from pregnancy through childhood. Parents were surveyed for use of prescription and over-the-counter drugs, and natural health products (NHPs, including homeopathic products and vitamins) at 3, 6, and 12 months after delivery. By one year of age, 96.0% of children had taken at least one pharmaceutical product. Among 307 reported products, 32 were given to at least 1% of cohort infants. Vitamin D, acetaminophen, ibuprofen, topical hydrocortisone, amoxicillin, and nystatin were the most common medications and natural health products (NHPs) received, with 8/32 of the most frequently used products being NHPs. Overall, 14.7% of pharmaceutical products administered to children were off-label and 35.8% were NHPs or products without a Drug Identification Number (DIN). The use of over-the-counter medications and NHPs is common and off-label use of drugs is frequent, even in the first year of life. This study highlights the importance of conducting studies on medication use in infants, and of infant medication use monitoring by healthcare providers.

Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks.

Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).

The Pseudomonas aeruginosa whole genome sequence: A 20th anniversary celebration.

Toward the end of August 2000, the 6.3 Mbp whole genome sequence of Pseudomonas aeruginosa strain PAO1 was published. With 5570 open reading frames (ORFs), PAO1 had the largest microbial genome sequenced up to that point in time-including a large proportion of metabolic, transport and antimicrobial resistance genes supporting its ability to colonize diverse environments. A remarkable 9% of its ORFs were predicted to encode proteins with regulatory functions, providing new insight into bacterial network complexity as a function of network size. In this celebratory article, we fast forward 20 years, and examine how access to this resource has transformed our understanding of P. aeruginosa. What follows is more than a simple review or commentary; we have specifically asked some of the leaders in the field to provide personal reflections on how the PAO1 genome sequence, along with the Pseudomonas Community Annotation Project (PseudoCAP) and Pseudomonas Genome Database (pseudomonas.com), have contributed to the many exciting discoveries in this field. In addition to bringing us all up to date with the latest developments, we also ask our contributors to speculate on how the next 20 years of Pseudomonas research might pan out.

Composition and Associations of the Infant Gut Fungal Microbiota with Environmental Factors and Childhood Allergic Outcomes.

Although often neglected in gut microbiota studies, recent evidence suggests that imbalanced, or dysbiotic, gut mycobiota (fungal microbiota) communities in infancy coassociate with states of bacterial dysbiosis linked to inflammatory diseases such as asthma. In the present study, we (i) characterized the infant gut mycobiota at 3 months and 1 year of age in 343 infants from the CHILD Cohort Study, (ii) defined associations among gut mycobiota community composition and environmental factors for the development of inhalant allergic sensitization (atopy) at age 5 years, and (iii) built a predictive model for inhalant atopy status at age 5 years using these data. We show that in Canadian infants, fungal communities shift dramatically in composition over the first year of life. Early-life environmental factors known to affect gut bacterial communities were also associated with differences in gut fungal community alpha diversity, beta diversity, and/or the relative abundance of specific fungal taxa. Moreover, these metrics differed among healthy infants and those who developed inhalant allergic sensitization (atopy) by age 5 years. Using a rationally selected set of early-life environmental factors in combination with fungal community composition at 1 year of age, we developed a machine learning logistic regression model that predicted inhalant atopy status at 5 years of age with 81% accuracy. Together, these data suggest an important role for the infant gut mycobiota in early-life immune development and indicate that early-life behavioral or therapeutic interventions have the potential to modify infant gut fungal communities, with implications for an infant's long-term health. IMPORTANCE Recent evidence suggests an immunomodulatory role for commensal fungi (mycobiota) in the gut, yet little is known about the composition and dynamics of early-life gut fungal communities. In this work, we show for the first time that the composition of the gut mycobiota of Canadian infants changes dramatically over the course of the first year of life, is associated with environmental factors such as geographical location, diet, and season of birth, and can be used in conjunction with knowledge of a small number of key early-life factors to predict inhalant atopy status at age 5 years. Our study highlights the importance of considering fungal communities as indicators or inciters of immune dysfunction preceding the onset of allergic disease and can serve as a benchmark for future studies aiming to examine infant gut fungal communities across birth cohorts.

Bacterial cyclic diguanylate signaling networks sense temperature.

Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change. Analyses using protein chimeras indicate that heat-sensing is mediated by a thermosensitive Per-Arnt-SIM (PAS) domain. TdcA homologs are widespread in sequence databases, and a distantly related, heterologously expressed homolog from the Betaproteobacteria order Gallionellales also displayed thermosensitive diguanylate cyclase activity. We propose, therefore, that thermotransduction is a conserved function of c-di-GMP signaling networks, and that thermosensitive catalysis of a second messenger constitutes a mechanism for thermal sensing in bacteria.

PSORTdb 4.0: expanded and redesigned bacterial and archaeal protein subcellular localization database incorporating new secondary localizations.

Protein subcellular localization (SCL) is important for understanding protein function, genome annotation, and aids identification of potential cell surface diagnostic markers, drug targets, or vaccine components. PSORTdb comprises ePSORTdb, a manually curated database of experimentally verified protein SCLs, and cPSORTdb, a pre-computed database of PSORTb-predicted SCLs for NCBI's RefSeq deduced bacterial and archaeal proteomes. We now report PSORTdb 4.0 (http://db.psort.org/). It features a website refresh, in particular a more user-friendly database search. It also addresses the need to uniquely identify proteins from NCBI genomes now that GI numbers have been retired. It further expands both ePSORTdb and cPSORTdb, including additional data about novel secondary localizations, such as proteins found in bacterial outer membrane vesicles. Protein predictions in cPSORTdb have increased along with the number of available microbial genomes, from approximately 13 million when PSORTdb 3.0 was released, to over 66 million currently. Now, analyses of both complete and draft genomes are included. This expanded database will be of wide use to researchers developing SCL predictors or studying diverse microbes, including medically, agriculturally and industrially important species that have both classic or atypical cell envelope structures or vesicles.

Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies.

BACKGROUND: Childhood asthma incidence is decreasing in some parts of Europe and North America. Antibiotic use in infancy has been associated with increased asthma risk. In the present study, we tested the hypothesis that decreases in asthma incidence are linked to reduced antibiotic prescribing and mediated by changes in the gut bacterial community. METHODS: This study comprised population-based and prospective cohort analyses. At the population level, we used administrative data from British Columbia, Canada (population 4·7 million), on annual rates of antibiotic prescriptions and asthma diagnoses, to assess the association between antibiotic prescribing (at age <1 year) and asthma incidence (at age 1-4 years). At the individual level, 2644 children from the Canadian Healthy Infant Longitudinal Development (CHILD) prospective birth cohort were examined for the association of systemic antibiotic use (at age <1 year) with the diagnosis of asthma (at age 5 years). In the same cohort, we did a mechanistic investigation of 917 children with available 16S rRNA gene sequencing data from faecal samples (at age ≤1 year), to assess how composition of the gut microbiota relates to antibiotic exposure and asthma incidence. FINDINGS: At the population level between 2000 and 2014, asthma incidence in children (aged 1-4 years) showed an absolute decrease of 7·1 new diagnoses per 1000 children, from 27·3 (26·8-28·3) per 1000 children to 20·2 (19·5-20·8) per 1000 children (a relative decrease of 26·0%). Reduction in incidence over the study period was associated with decreasing antibiotic use in infancy (age <1 year), from 1253·8 prescriptions (95% CI 1219·3-1288·9) per 1000 infants to 489·1 (467·6-511·2) per 1000 infants (Spearman's r=0·81; p<0·0001). Asthma incidence increased by 24% with each 10% increase in antibiotic prescribing (adjusted incidence rate ratio 1·24 [95% CI 1·20-1·28]; p<0·0001). In the CHILD cohort, after excluding children who received antibiotics for respiratory symptoms, asthma diagnosis in childhood was associated with infant antibiotic use (adjusted odds ratio [aOR] 2·15 [95% CI 1·37-3·39]; p=0·0009), with a significant dose-response; 114 (5·2%) of 2182 children unexposed to antibiotics had asthma by age 5 years, compared with 23 (8·1%) of 284 exposed to one course, five (10·2%) of 49 exposed to two courses, and six (17·6%) of 34 exposed to three or more courses (aOR 1·44 [1·16-1·79]; p=0·0008). Increasing α-diversity of the gut microbiota, defined as an IQR increase (25th to 75th percentile) in the Chao1 index, at age 1 year was associated with a 32% reduced risk of asthma at age 5 years (aOR for IQR increase 0·68 [0·46-0·99]; p=0·046). In a structural equation model, we found the gut microbiota at age 1 year, characterised by α-diversity, ß-diversity, and amplicon sequence variants modified by antibiotic exposure, to be a significant mediator between outpatient antibiotic exposure in the first year of life and asthma diagnosis at age 5 years (ß=0·08; p=0·027). INTERPRETATION: Our findings suggest that the reduction in the incidence of paediatric asthma observed in recent years might be an unexpected benefit of prudent antibiotic use during infancy, acting via preservation of the gut microbial community. FUNDING: British Columbia Ministry of Health, Pharmaceutical Services Branch; Canadian Institutes of Health Research; Allergy, Genes and Environment (AllerGen) Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

AB569, a nontoxic chemical tandem that kills major human pathogenic bacteria.

Antibiotic-resistant superbug bacteria represent a global health problem with no imminent solutions. Here we demonstrate that the combination (termed AB569) of acidified nitrite (A-NO2-) and Na2-EDTA (disodium ethylenediaminetetraacetic acid) inhibited all Gram-negative and Gram-positive bacteria tested. AB569 was also efficacious at killing the model organism Pseudomonas aeruginosa in biofilms and in a murine chronic lung infection model. AB569 was not toxic to human cell lines at bactericidal concentrations using a basic viability assay. RNA-Seq analyses upon treatment of P. aeruginosa with AB569 revealed a catastrophic loss of the ability to support core pathways encompassing DNA, RNA, protein, ATP biosynthesis, and iron metabolism. Electrochemical analyses elucidated that AB569 produced more stable SNO proteins, potentially explaining one mechanism of bacterial killing. Our data implicate that AB569 is a safe and effective means to kill pathogenic bacteria, suggesting that simple strategies could be applied with highly advantageous therapeutic/toxicity index ratios to pathogens associated with a myriad of periepithelial infections and related disease scenarios.

High-throughput detection of RNA processing in bacteria.

BACKGROUND: Understanding the RNA processing of an organism's transcriptome is an essential but challenging step in understanding its biology. Here we investigate with unprecedented detail the transcriptome of Pseudomonas aeruginosa PAO1, a medically important and innately multi-drug resistant bacterium. We systematically mapped RNA cleavage and dephosphorylation sites that result in 5'-monophosphate terminated RNA (pRNA) using monophosphate RNA-Seq (pRNA-Seq). Transcriptional start sites (TSS) were also mapped using differential RNA-Seq (dRNA-Seq) and both datasets were compared to conventional RNA-Seq performed in a variety of growth conditions. RESULTS: The pRNA-Seq library revealed known tRNA, rRNA and transfer-messenger RNA (tmRNA) processing sites, together with previously uncharacterized RNA cleavage events that were found disproportionately near the 5' ends of transcripts associated with basic bacterial functions such as oxidative phosphorylation and purine metabolism. The majority (97%) of the processed mRNAs were cleaved at precise codon positions within defined sequence motifs indicative of distinct endonucleolytic activities. The most abundant of these motifs corresponded closely to an E. coli RNase E site previously established in vitro. Using the dRNA-Seq library, we performed an operon analysis and predicted 3159 potential TSS. A correlation analysis uncovered 105 antiparallel pairs of TSS that were separated by 18 bp from each other and were centered on single palindromic TAT(A/T)ATA motifs (likely - 10 promoter elements), suggesting that, consistent with previous in vitro experimentation, these sites can initiate transcription bi-directionally and may thus provide a novel form of transcriptional regulation. TSS and RNA-Seq analysis allowed us to confirm expression of small non-coding RNAs (ncRNAs), many of which are differentially expressed in swarming and biofilm formation conditions. CONCLUSIONS: This study uses pRNA-Seq, a method that provides a genome-wide survey of RNA processing, to study the bacterium Pseudomonas aeruginosa and discover extensive transcript processing not previously appreciated. We have also gained novel insight into RNA maturation and turnover as well as a potential novel form of transcription regulation. NOTE: All sequence data has been submitted to the NCBI sequence read archive. Accession numbers are as follows: [NCBI sequence read archive: SRX156386, SRX157659, SRX157660, SRX157661, SRX157683 and SRX158075]. The sequence data is viewable using Jbrowse on www.pseudomonas.com .

IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets.

IslandViewer (http://www.pathogenomics.sfu.ca/islandviewer/) is a widely-used webserver for the prediction and interactive visualization of genomic islands (GIs, regions of probable horizontal origin) in bacterial and archaeal genomes. GIs disproportionately encode factors that enhance the adaptability and competitiveness of the microbe within a niche, including virulence factors and other medically or environmentally important adaptations. We report here the release of IslandViewer 4, with novel features to accommodate the needs of larger-scale microbial genomics analysis, while expanding GI predictions and improving its flexible visualization interface. A user management web interface as well as an HTTP API for batch analyses are now provided with a secured authentication to facilitate the submission of larger numbers of genomes and the retrieval of results. In addition, IslandViewer's integrated GI predictions from multiple methods have been improved and expanded by integrating the precise Islander method for pre-computed genomes, as well as an updated IslandPath-DIMOB for both pre-computed and user-supplied custom genome analysis. Finally, pre-computed predictions including virulence factors and antimicrobial resistance are now available for 6193 complete bacterial and archaeal strains publicly available in RefSeq. IslandViewer 4 provides key enhancements to facilitate the analysis of GIs and better understand their role in the evolution of successful environmental microbes and pathogens.

Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database.

The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using community-based approaches including a major new Pseudomonas community initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.

Clinical utilization of genomics data produced by the international Pseudomonas aeruginosa consortium.

The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aeruginosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P. aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care.

IslandViewer 3: more flexible, interactive genomic island discovery, visualization and analysis.

IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. We now report a major new release of IslandViewer, since the last release in 2013. IslandViewer 3 incorporates a completely new genome visualization tool, IslandPlot, enabling for the first time interactive genome analysis and gene search capabilities using synchronized circular, horizontal and vertical genome views. In addition, more curated virulence factors and antimicrobial resistance genes have been incorporated, and homologs of these genes identified in closely related genomes using strict filters. Pathogen-associated genes have been re-calculated for all pre-computed complete genomes. For user-uploaded genomes to be analysed, IslandViewer 3 can also now handle incomplete genomes, with an improved queuing system on compute nodes to handle user demand. Overall, IslandViewer 3 represents a significant new version of this GI analysis software, with features that may make it more broadly useful for general microbial genome analysis and visualization.

Mining the Pseudomonas genome.

Pseudomonas species were targeted early for genomic studies since they were noted for their diverse metabolic capacity, ability to inhabit a wide range of environments and hosts, and include notable human and agriculturally relevant pathogens. As more genomes are sequenced, the power of genome-scale analyses are increasing and a wide range of analyses are now possible. The Pseudomonas Genome database has contributed to this effort by providing peer-reviewed, continually updated annotations of the Pseudomonas aeruginosa PAO1 reference strain genome plus integrated data and analyses of related Pseudomonas species. Analyses are now available via multiple resources to facilitate identification and characterization of drug targets, virulence factors, regulatory elements, genomic islands, genome rearrangements, orthologs, single nucleotide polymorphisms, and multiple other gene/protein-based analyses from gene expression to protein structure. We describe here how the Pseudomonas Genome Database and other bioinformatics resources can be leveraged to help Pseudomonas researchers "mine" Pseudomonas genomes, and associated genome-scale data, to facilitate new discovery.

InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation.

InnateDB (http://www.innatedb.com) is an integrated analysis platform that has been specifically designed to facilitate systems-level analyses of mammalian innate immunity networks, pathways and genes. In this article, we provide details of recent updates and improvements to the database. InnateDB now contains >196 000 human, mouse and bovine experimentally validated molecular interactions and 3000 pathway annotations of relevance to all mammalian cellular systems (i.e. not just immune relevant pathways and interactions). In addition, the InnateDB team has, to date, manually curated in excess of 18 000 molecular interactions of relevance to innate immunity, providing unprecedented insight into innate immunity networks, pathways and their component molecules. More recently, InnateDB has also initiated the curation of allergy- and asthma-related interactions. Furthermore, we report a range of improvements to our integrated bioinformatics solutions including web service access to InnateDB interaction data using Proteomics Standards Initiative Common Query Interface, enhanced Gene Ontology analysis for innate immunity, and the availability of new network visualizations tools. Finally, the recent integration of bovine data makes InnateDB the first integrated network analysis platform for this agriculturally important model organism.

OrtholugeDB: a bacterial and archaeal orthology resource for improved comparative genomic analysis.

Prediction of orthologs (homologous genes that diverged because of speciation) is an integral component of many comparative genomics methods. Although orthologs are more likely to have similar function versus paralogs (genes that diverged because of duplication), recent studies have shown that their degree of functional conservation is variable. Also, there are inherent problems with several large-scale ortholog prediction approaches. To address these issues, we previously developed Ortholuge, which uses phylogenetic distance ratios to provide more precise ortholog assessments for a set of predicted orthologs. However, the original version of Ortholuge required manual intervention and was not easily accessible; therefore, we now report the development of OrtholugeDB, available online at http://www.pathogenomics.sfu.ca/ortholugedb. OrtholugeDB provides ortholog predictions for completely sequenced bacterial and archaeal genomes from NCBI based on reciprocal best Basic Local Alignment Search Tool hits, supplemented with further evaluation by the more precise Ortholuge method. The OrtholugeDB web interface facilitates user-friendly and flexible ortholog analysis, from single genes to genomes, plus flexible data download options. We compare Ortholuge with similar methods, showing how it may more consistently identify orthologs with conserved features across a wide range of taxonomic distances. OrtholugeDB facilitates rapid, and more accurate, bacterial and archaeal comparative genomic analysis and large-scale ortholog predictions.

Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes.

Pseudomonas is a metabolically-diverse genus of bacteria known for its flexibility and leading free living to pathogenic lifestyles in a wide range of hosts. The Pseudomonas Genome Database (http://www.pseudomonas.com) integrates completely-sequenced Pseudomonas genome sequences and their annotations with genome-scale, high-precision computational predictions and manually curated annotation updates. The latest release implements an ability to view sequence polymorphisms in P. aeruginosa PAO1 versus other reference strains, incomplete genomes and single gene sequences. This aids analysis of phenotypic variation between closely related isolates and strains, as well as wider population genomics and evolutionary studies. The wide range of tools for comparing Pseudomonas annotations and sequences now includes a strain-specific access point for viewing high precision computational predictions including updated, more accurate, protein subcellular localization and genomic island predictions. Views link to genome-scale experimental data as well as comparative genomics analyses that incorporate robust genera-geared methods for predicting and clustering orthologs. These analyses can be exploited for identifying putative essential and core Pseudomonas genes or identifying large-scale evolutionary events. The Pseudomonas Genome Database aims to provide a continually updated, high quality source of genome annotations, specifically tailored for Pseudomonas researchers, but using an approach that may be implemented for other genera-level research communities.