Welcome #TeamVirulence, left to right: Rachel Tran (Biochem 3R06), Sally Min (BiomedDC 4A15), Anatoly Miroshnichencko (BiomedDC 4A15), and Rafik El Werfalli (BiomedDC 4A15), who are collectively working on development of CARD:Virulence, a new branch of the Comprehensive Antibiotic Resistance Database dedicated to the molecular surveillance of bacterial virulence factors.
- Alcock, B., A.R. Raphenya, A.N. Sharma, K.K. Tsang, T.T.Y. Lau, A. Hernandez-Koutoucheva, & A.G. McArthur. 2018. Data and curation in the Comprehensive Antibiotic Resistance Database. Poster presentation at the Canadian Society of Microbiologists Annual Meeting, Winnipeg, Manitoba.
- Lau, T.T.Y., A.R. Raphenya, B. Alcock, & A.G. McArthur. 2018. Optimizing antimicrobial resistance surveillance tools through biological data organization and taxonomic identification of resistance genes. Poster presentation at the Canadian Society of Microbiologists Annual Meeting, Winnipeg, Manitoba.
- Maguire, F., A.R. Raphenya, B. Alcock, A.G. McArthur, F.S. Brinkman, & R.G. Beiko. 2018. The cost of speed: evaluating systematic failures in metagenomic AMR profiling. Poster presentation at the Canadian Society of Microbiologists Annual Meeting, Winnipeg, Manitoba.
- Raphenya, A.R., B. Alcock, K.K. Tsang, A.N. Sharma, T.T.Y. Lau, A. Hernandez-Koutoucheva, & A.G. McArthur. 2018. The Comprehensive Antibiotic Resistance Database and the Resistance Gene Identifier – Prediction of antimicrobial resistance genes and mutations for genomic and metagenomic sequencing data. Oral presentation at the Canadian Society of Microbiologists Annual Meeting, Winnipeg, Manitoba.
- Tsang, K.K., H. Zubyk, S. Chou, G.D. Wright, & A.G. McArthur. Decoding bad bags: Predicting antibiotic resistance phenotypes from genotype. Oral presentation at the Canadian Society of Microbiologists Annual Meeting, Winnipeg, Manitoba.
Kara Tsang passed her graduate transfer exam today, officially moving from the McMaster Biochemistry & Biomedical Sciences Masters program to the Ph.D. program. Kara’s work focusses on the intersection of biocuration, bioinformatics, machine learning, mutant screening, and phenotypic testing for prediction antimicrobial resistance phenotype from genotype. Well done Kara!
Update: Hot on the heels of becoming a Ph.D. student, Kara has won a 2018/2019 Department of Biochemistry & Biomedical Sciences’s Fred and Helen Knight Enrichment Award!
The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
CARD Curation: Addition of HERA, TRU, & ACI beta-lactamases, sul4, and new quinolone efflux pumps.
Antibiotic Resistance Ontology: Expanded to include an entirely new branch describing AMR phenotypic testing methods. ARO additionally now officially available at the OBO Foundry, allowing formal integration with other ontological resources, most notably the Genomic Epidemiology Application Ontology (GenEpiO), https://github.com/genepio/genepio.
Resistance Gene Identifier: Resistome prediction for low quality or low coverage assemblies, merged metagenomics reads, and small plasmids or assembly contigs. Includes prediction of partial AMR genes. Support added for Docker operating-system-level virtualization (i.e. containerization).
Prevalence, Resistomes, & Variants: Expanded to 67 important pathogens, with a focus on ESKAPEs, WHO Priority Pathogens, and agents of sepsis.
The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
This February 2018 release is our largest to date and includes new data types, a new classification system, an entirely new version of the Resistance Gene Identifier, and website improvements.
CARD Curation: 37 new ADC beta-lactamases, 21 PDC beta-lactamases, new MCR proteins, 23 rRNA mutations, resistant isoleucyl-tRNA synthetases, hundreds of new resistance mutations, and more. While in past releases all curated AMR mutations were those characterized from clinical isolates, CARD now additionally includes mutations discovered via in vitro selection experiments. Ontological improvements have been made to enable an entirely new classification system for CARD data and RGI results: resistance determinants are now systematically categorized by AMR Gene Family, Drug Class, and Resistance Mechanism. The Antibiotic Resistance Ontology is now additionally available via GitHub, https://github.com/arpcard.
Resistance Gene Identifier: Entirely new codebase, compatible with CARD data (card.json) version 2.0.0 and up (download separately). Open Reading Frame (ORF) prediction using Prodigal, homolog detection using BLAST (default) or DIAMOND, and Strict significance based on CARD curated bitscore cut-offs. Addition of rRNA mutation and efflux over-expression models. Hits of 95% identity or better are automatically listed as Strict. All results organized by revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Revised documentation, command line menu, and website graphical interface. The Resistance Gene Identifier is now additionally available via GitHub, https://github.com/arpcard.
Prevalence, Genomes, & Variants: Expansion of our computer-generated data set on the prevalence of AMR genes and variants among the sequenced genomes, plasmids, and whole-genome shotgun assemblies available at NCBI for clinically important pathogens. CARD Prevalence 2.0.0 is based on sequence data acquired from NCBI on August 28, 2017, analyzed using RGI 4.0.0 (DIAMOND homolog detection) and CARD 2.0.0. Now includes results for protein overexpression models and rRNA mutations. All results organized by the revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Download files now include 35000+ genome annotations and all predicted sequence variants.
Tsang, K.K. & A.G. McArthur. 2017. Encoding the efflux pump phenomena. Oral presentation at the Second American Society for Microbiology Meeting on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatics Pipelines, Washington, D.C.
Background: Efflux pumps are a major mechanism for intrinsic and acquired resistance to our current antibiotic armamentarium. Efflux mechanisms interplay synergistically with other resistance mechanisms, including drug permeability, degradation and inactivation, to strengthen pathogen antimicrobial resistance levels. Despite their clinical relevance, there is no resource that seeks to understand and predict the contribution of efflux pumps in antimicrobial resistance from genome sequence. This has resulted in limited prediction of the full potential of all resistance determinants in a bacterial cell.
Methods: The Comprehensive Antibiotic Resistance Database (CARD, https://card.mcmaster.ca/) and Resistance Gene Identifier (RGI) were optimized for E. coli and P. aeruginosa efflux pump detection through extensive curation and algorithmic development. Literature was mined and analyzed to curate all published information on E. coli and P. aeruginosa efflux pumps into CARD. Algorithmic development of RGI involved creating bioinformatics detection models and refining their parameters. The Efflux Pump Identifier (EPI) was developed to predict efflux pumps and antimicrobial resistance based on RGI results generated using CARD and tested using genome sequences of characterized, clinical multi-drug resistant E. coli and P. aeruginosa isolates.
Results: The Efflux Pump Identifier (EPI) analyzed 124 E. coli and 94 P. aeruginosa clinical multi-drug resistant samples to predict efflux pumps and their complex regulatory networks under three paradigms: 1) Perfect, 2) Partial, and 3) Putative. The Perfect paradigm identifies perfect matches to known efflux pumps curated into CARD. The Partial algorithm detects efflux pumps where at least one or more components of the efflux pump is not a perfect match to an efflux pump component in CARD, but likely a functional homolog. Lastly, the Putative algorithm discovers potential efflux pumps where all components are not perfect matches to previously curated components in CARD.
Conclusions: The development of the Efflux Pump Identifier (EPI) devotes effort to an area in antimicrobial resistance where insufficient attention has been paid in the past. This is a step towards answering the long-standing question in the efflux pump phenomena; is the detected efflux pump genotype being expressed to present a specific phenotype? Using the Efflux Pump Identifier (EPI) in tandem with the existing repertoire of detection tools for dedicated and mutational resistance determinants leads to the complete prediction of antibiogram from genome sequence.
Today we say farewell to Arjun Sharma & Suman Virdee. Arjun joined the lab as a second year volunteer, staying to perform a Biochem 3R06 research project. He has been very active in our Comprehensive Antibiotic Resistance Database project, co-developing our CARD*Shark text mining tools for computer-guided curation of literature in PubMed, pipelines for our clinical isolate genome sequencing work, and developing novel algorithms for predicting glycopeptide resistance from genome assemblies. He was the recipient of an IIDR Summer Student Fellowship and leaves the Biochemistry program to enter medical school at the University of Toronto. Suman joined the lab in the 4th year of the Biomedical Discovery & Commercialization program, performing her thesis research on RNA-Seq bioinformatics workflows in a collaboration between our lab and the laboratory of Dr. Kristen Hope (McMaster Stem Cell and Cancer Research Institute), extending her research into the summer by winning a CIHR Summer Undergraduate Research Award. Suman finished her degree and this September starts in the McMaster Master of Science in Global Health program. Bon chance Suman & Arjun!
We haven’t been travelling much this year, but our collaborators have been busy!
Dearborn, D.C., A.B. Gager, A.G. McArthur, M.E. Gilmour, E. Mandzhukova, R.A. Mauck. 2017. How to get diverse MHC genotypes without disassortative mating. Presentation at the 2017 Annual Meeting of the Society for Integrative and Comparative Biology, New Orleans, Louisiana.
McLean, M., D. Theriault, M. Kelley, B.A. Lago, A.G. McArthur, & L. Williams. 2017. Role of Nfe2 and pro-oxidant exposure in inner ear development in zebrafish. Presentation at the Society of Toxicology 56rd Annual Meeting, Baltimore, Maryland.
Williams, L.M., B.A. Lago, A.G. McArthur, A.R. Raphenya, N. Pray, N. Saleem, S. Salas, K. Paulson, R.S. Mangar, Y. Liu, A.H. Vo, & J.A. Shavit. 2017. The transcription factor, Nuclear factor, erythroid 2 (Nfe2), is a regulator of the oxidative stress response during Danio rerio development. Presentation at the Society of Toxicology 56rd Annual Meeting, Baltimore, Maryland.
Winsor, G.L., C. Bertelli, K.K. Tsang, B. Alcock, A.G. McArthur, & F.S.L. Brinkman. 2017. Pseudomonas Genome Database 2017: Improved gene/AMR/VF/genomic island annotations, comparative genome analyses, and a platform for facilitating public health genomic epidemiology. Presentation at the 16th International Conference on Pseudomonas, Liverpool, United Kingdom.
The loss of effective antimicrobials is reducing our ability to protect the global population from infectious disease. However, the field of antibiotic drug discovery and the public health monitoring of antimicrobial resistance (AMR) is beginning to exploit the power of genome and metagenome sequencing. The creation of novel AMR bioinformatics tools and databases and their continued development will advance our understanding of the molecular mechanisms and threat severity of antibiotic resistance, while simultaneously improving our ability to accurately predict and screen for antibiotic resistance genes within environmental, agricultural, and clinical settings. To do so, efforts must be focused toward exploiting the advancements of genome sequencing and information technology. Currently, AMR bioinformatics software and databases reflect different scopes and functions, each with its own strengths and weaknesses. A review of the available tools reveals common approaches and reference data but also reveals gaps in our curated data, models, algorithms, and data-sharing tools that must be addressed to conquer the limitations and areas of unmet need within the AMR research field before DNA sequencing can be fully exploited for AMR surveillance and improved clinical outcomes.