Wright, G.D. & A.G. McArthur. 2015. A bioinformatic platform for the characterization of antibiotic resistance in bacterial genomes and metagenomes. Presentation at the 2015 Interscience Conference of Antimicrobial Agents and Chemotherapy, San Diego, California.
The increasingly routine sequencing of bacterial genomes in biomedical research and the clinical lab requires access to easy to use, efficient, and accurate bioinformatic tools for analysis of bacterial traits from virulence to drug resistance. To contribute to this growing need, we have developed a platform for the investigation of antibiotic resistance elements, the Comprehensive Antibiotic Resistance Database (http://arpcard.mcmaster.ca/). This resource includes a manually curated database of over 3000 resistance genes and associated literature, protein structures, and target antibiotics. Associated with this platform are tools to aid in the study of resistance including the Resistance Gene Identifier (RGI) that can analyze genomic data for the presence of resistance elements. Our goal is to accurately predict resistance phenotype from genomic data. Our analysis of many genomes and associated antibiograms reveals a reservoir of ‘silent’ resistance genes that are predicted to encode viable resistance elements yet the phenotype is drug sensitive. Our efforts to manage these issues along with identifying and adding new resistance genes will be presented.
Authors: McArthur AG, Wright GD. Curr Opin Microbiol. 2015 Jul 31;27:45-50.
Antimicrobial resistance is a global health challenge and has an evolutionary trajectory ranging from proto-resistance in the environment to untreatable clinical pathogens. Resistance is not static, as pathogenic strains can move among patient populations and individual resistance genes can move among pathogens. Effective treatment of resistant infections, antimicrobial stewardship, and new drug discovery increasingly rely upon genotype information, powered by decreasing costs of DNA sequencing. These new approaches will require advances in microbial informatics, particularly in development of reference databases of molecular determinants such as our Comprehensive Antibiotic Resistance Database and clinical metadata, new algorithms for prediction of resistome and resistance phenotype from genotype, and new protocols for global collection and sharing of high-throughput molecular epidemiology data.
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.
Amos Raphenya and Pearl Guo have joined the McArthur Lab! Amos graduated from McMaster with Bachelor of Computer Engineering in 2008 and joins the lab as a core software engineer, for both our drug resistance and ecotoxicogenomics projects. Pearl just finished her second year at the University of Waterloo’s Computer Science co-op program, with a minor in Bioinformatics. Pearl will be performing a 3 month co-op position in the lab, with a focus on algorithms for prediction of glycopeptide resistance.
McArthur, A.G., Waglechner, N., Nizam, F., Pereira, S.K., Jia, B., Sardar, D., Westman, E.L., Pawlowski, A.C., Johnson, T., Lo, R., Courtot, M., Brinkman, F.S., Williams, L.E., Frye, J.G., & Wright, G.D. 2015. The Comprehensive Antibiotic Resistance Database. Presentation at the 4th ASM Conference on Antimicrobial Resistance in Zoonotic Bacteria and Foodborne Pathogens, Washington, District of Columbia.
Antimicrobial resistance (AMR) is among the most pressing public health crises of the 21st Century. Despite the importance of resistance to health, this field has been slow to take advantage of genome scale tools. Rather, phenotype based criteria dominate the epidemiology of antibiotic action and effectiveness. As a result, there is a poor understanding of which antibiotic resistance genes are in circulation, which ones are a threat, and how clinicians and public health workers can manage the crisis of resistance. However, DNA sequencing is rapidly decreasing in cost and as such we are on the cusp of an age of high-throughput molecular epidemiology. What are needed are tools for rapid, accurate analysis of DNA sequence data for the genetic underpinnings of antibiotic resistance. In an effort to address this problem, we have created the Comprehensive Antibiotic Resistance Database (arpcard.mcmaster.ca). This database is a rigorously curated collection of known antibiotics, targets, and resistance determinants. It integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in raw genome sequences using the novel Resistance Gene Identifier (RGI). Here we review the current state of the CARD, particularly recent advances in the curation of resistance determinants and the structure of the ARO. We will also present our plans for development of semi- and fully-automated text mining algorithms for curation of broader AMR data, construction of Probabilistic Graphic Models for improved AMR phenotype prediction, and development of portable command-line genome analysis tools.
The McArthur Lab welcomes Tariq Elsayegh from the Royal College of Surgeons in Ireland, where he is in the six year medical program. Tariq is undertaking a 6 week project to build bioinformatics models for colistin resistance in the Comprehensive Antibiotic Resistance Database.
Justin Jia (left) and Daim Sardar (right) have joined the McArthur Lab. Justin has joined as a 8 month McMaster Honours Biochemistry – Biomedical Research Specialization Co-Op placement and will be working as a Comprehensive Antibiotic Resistance Database biocurator. Daim will be in the lab 8 weeks performing an Independent Project on 16S rRNA mutations conferring antibiotic resistance as part of his Honours Integrated Science Program (iSci) 3A12 coursework.
Authors: AG McArthur, N Waglechner, F Nizam, A Yan, MA Azad, AJ Baylay, K Bhullar, MJ Canova, G De Pascale, L Ejim, L Kalan, AM King, K Koteva, M Morar, MR Mulvey, JS O’Brien, AC Pawlowski, LJV Piddock, P Spanogiannopoulos, AD Sutherland, I Tang, PL Taylor, M Thaker, W Wang, M Yan, T Yu, & GD Wright
The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.