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.
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.
Although considerable progress has been made in dissecting the signaling pathways involved in the innate immune response, it is now apparent that this response can no longer be productively thought ...of in terms of simple linear pathways. InnateDB (www.innatedb.ca) has been developed to facilitate systems‐level analyses that will provide better insight into the complex networks of pathways and interactions that govern the innate immune response. InnateDB is a publicly available, manually curated, integrative biology database of the human and mouse molecules, experimentally verified interactions and pathways involved in innate immunity, along with centralized annotation on the broader human and mouse interactomes. To date, more than 3500 innate immunity‐relevant interactions have been contextually annotated through the review of 1000 plus publications. Integrated into InnateDB are novel bioinformatics resources, including network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user‐supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, which will enable biologists without a computational background to explore their data in a more systems‐oriented manner.
Synopsis
The importance of the innate immune response has long been recognized in the front line of defense against invading pathogens. If not tightly regulated, however, an overwhelming immune response can lead to what is sometimes called a cytokine storm. One such out‐of‐control response, sepsis, results in more than 200 000 deaths a year in the United States alone (Angus et al, 2001). Over the course of the last decade, significant progress has been made in understanding the innate immune response, including the detailed dissection of some of the critical signaling pathways involved (Lang and Mansell, 2007; Matsukawa, 2007). Despite these efforts, many questions remain unanswered including how the innate immune system initiates distinct responses toward particular pathogens. It is becoming increasingly clear that the innate immune response does not involve simple linear pathways but rather complex networks of pathways and interactions, positive and negative feedback loops and multifaceted transcriptional responses (Tegner et al, 2006; Lee and Kim, 2007). To better understand the complexities of the innate immune response and the cross‐talk between its components, complementary systems‐level analyses and more focused follow‐up experimental approaches are now needed.
Recently, researchers have started to apply systems biology approaches to the study of the immune system (Gilchrist et al, 2006; Tegner et al, 2006; Andersen et al, 2008) and bioinformatics resources are now emerging to aid these types of analyses. Despite the enormous efforts of the major publicly available interaction and pathway databases to provide as wide‐ranging cover as possible (Salwinski et al, 2004; Alfarano et al, 2005; Joshi‐Tope et al, 2005; Breitkreutz et al, 2007; Chatr‐aryamontri et al, 2007; Kanehisa et al, 2007; Kerrien et al, 2007), it was quickly apparent to us that currently available bioinformatics resources provided poor coverage and detail of the molecular interactions and pathways relevant to innate immunity, information that is essential for the systems‐orientated interpretation of large‐scale genomics data.
To overcome these problems and to provide a resource that will enable biologists without a computational background to explore their data in a more systems‐oriented manner, we have developed InnateDB. InnateDB (www.innatedb.ca) is a publicly available database and analysis platform for the genes, proteins, experimentally verified interactions and pathways involved in the human and murine innate immune responses.
One of the primary goals of InnateDB is to provide a manually curated centralized resource for experimentally verified human and mouse protein, gene and RNA molecular interactions involved in the innate immune system. To do this, a dedicated full‐time team of curators has been assembled to review the relevant biomedical literature and to submit detailed annotation on these interactions and pathways to InnateDB through customized submission system software. To date, more than 3500 innate immunity‐relevant interactions, involving around 1000 genes, have been manually curated through the review of approximately 1000 publications. Only interactions with published direct experimental evidence of a physical or biochemical interaction are submitted to InnateDB. The importance of manual curation is clear, as we are often able to double the number of interactions for a given gene or protein compared to the number currently present in the other interaction databases combined. Furthermore, this detailed manual curation permitted us to richly annotate these interactions and to place them in their relevant context. Interaction data in InnateDB are also curated, stored and downloadable in the Proteomics Standards Initiative Molecular Interaction (PSI‐MI) 2.5‐compliant XML format (Hermjakob et al, 2004).
In addition to the detailed manual curation of the genes, proteins and their interactions and pathways that are specifically known to have a role in the innate immune response, InnateDB also incorporates data on the entire human and mouse interactomes. To do this, annotation on more than 100 000 human and mouse interactions was integrated from several of the major publicly available interaction databases into InnateDB (Figure 1). To enable the investigation of genes, proteins and their molecular interactions that are relevant to particular pathways, InnateDB also includes cross‐references of genes not only to innate immunity‐relevant pathways but also to more than 2500 pathways from several of the major publicly available pathway databases (Figure 1). Detailed gene and protein annotation has also been extracted from a variety of other data sources.
Specific interactions, pathways and genes or proteins of interest can be interactively searched for in InnateDB through the flexible web‐based search interface of the database, providing a knowledge base for the community, whereas the bioinformatics and network visualization tools incorporated into InnateDB elevate the system from database to robust analysis platform. InnateDB allows one to integrate quantitative data (such as differential gene expression) into a molecular interaction network and pathway context, enabling the interrogation of such data in novel and insightful ways. Investigating differentially expressed molecular interaction networks may identify subnetworks or as‐yet unidentified pathways as being significantly involved in the response to a particular stimulus. By incorporating Cytoscape into InnateDB, investigators are able to take a closer look at the interactions involved in these pathways or subnetworks, potentially identifying cross‐talk between key pathways, and highlighting the molecules that are the hubs of these networks. Our Cerebral plugin allows one to further extend this experience, visually interrogating quantitative data across multiple conditions in more biologically intuitive pathway‐like layouts of networks, which are generated using subcellular localization information.
Integrated pathway over‐representation analysis can identify those pathways that are significantly associated with differentially regulated genes, highlighting those pathways that are significantly altered in their gene expression. Through such pathway analysis, it is possible to identify common pathways that are involved in the innate immune response to particular infections, and to identify the common central regulators of these pathways as attractive targets for immune modulation. (Figure 4).
InnateDB, along with other emerging resources for bioinformatics and systems‐level analysis of immunology (Kelley et al, 2005; Ortutay and Vihinen, 2006; Hijikata et al, 2007; Korb et al, 2008), will undoubtedly lead to novel and much deeper insights into the innate immune response to particular pathogens.
InnateDB is a molecular interaction and pathway database and analysis platform that has been developed to facilitate systems level analyses of the complex networks of pathways and interactions that govern the innate immune response, the wider immune system and the entire mammalian interactome.
To date, more than 3,500 innate immunity relevant interactions have been contextually annotated through the review of 1,000 plus publications.
Integrated into InnateDB are novel bioinformatics resources including, network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user‐supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, that will enable biologists without a computational background to explore their data in a more systems‐oriented, yet user‐friendly, manner.
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.
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.
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.
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).
Using the Pseudomonas aeruginosa Genome Project as a test case, we have developed a database and submission system to facilitate a community-based approach to continually updated genome annotation ...(http://www.pseudomonas.com). Researchers submit proposed annotation updates through one of three web-based form options which are then subjected to review, and if accepted, entered into both the database and log file of updates with author acknowledgement. In addition, a coordinator continually reviews literature for suitable updates, as we have found such reviews to be the most efficient. Both the annotations database and updates-log database have Boolean search capability with the ability to sort results and download all data or search results as tab-delimited files. To complement this peer-reviewed genome annotation, we also provide a linked GBrowse view which displays alternate annotations. Additional tools and analyses are also integrated, including PseudoCyc, and knockout mutant information. We propose that this database system, with its focus on facilitating flexible queries of the data and providing access to both peer-reviewed annotations as well as alternate annotation information, may be a suitable model for other genome projects wishing to use a continually updated, community-based annotation approach. The source code is freely available under GNU General Public Licence.
Abstract
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.
Mining the Pseudomonas genome Winsor, Geoffrey L; Brinkman, Fiona S L
Methods in molecular biology (Clifton, N.J.),
2014, Letnik:
1149
Journal Article
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.
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