Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, Min SY, Miroshnichenko A, Tran H-K, Werfalli RE, Nasir JA, Oloni M, Speicher DJ, Florescu A, Singh B, Faltyn M, Hernandez-Koutoucheva A, Sharma AN, Bordeleau E, Pawlowski AC, Zubyk HL, Dooley D, Griffiths E, Maguire F, Winsor GL, Beiko RG, Brinkman FSL, Hsiao WWL, Van Domselaar G, McArthur AG.
The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.
The identification and association of the nucleotide sequences encoding antibiotic resistance elements is critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, which are both heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probeset on DNA libraries generated from multi-drug resistant bacteria to selectively capture resistance genes reproducibly produced higher reads on-target at greater length of coverage when compared to shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables sensitive gene detection in diverse environments where antibiotic resistance represents less than 0.1% of the metagenome.
Glycopeptide antibiotics are produced by Actinobacteria through biosynthetic gene clusters that include genes supporting their regulation, synthesis, export and resistance. The chemical and biosynthetic diversities of glycopeptides are the product of an intricate evolutionary history. Extracting this history from genome sequences is difficult as conservation of the individual components of these gene clusters is variable and each component can have a different trajectory. We show that glycopeptide biosynthesis and resistance in Actinobacteria maps to approximately 150-400 million years ago. Phylogenetic reconciliation reveals that the precursors of glycopeptide biosynthesis are far older than other components, implying that these clusters arose from a pre-existing pool of genes. We find that resistance appeared contemporaneously with biosynthetic genes, raising the possibility that the mechanism of action of glycopeptides was a driver of diversification in these gene clusters. Our results put antibiotic biosynthesis and resistance into an evolutionary context and can guide the future discovery of compounds possessing new mechanisms of action, which are especially needed as the usefulness of the antibiotics available at present is imperilled by human activity.
More details at McMaster’s Brighter World.
Dr. McArthur and PhD student Kara Tsang taught together at the 2019 MacData Institute Summer School, with Dr. McArthur reviewing biocuration and bioinformatics for genomic surviellence of antimicrobial resistance and Kara following up with a lecture on machine learning techniques to predict clinical antimicrobial resistance from raw genomic sequence.
Also congratulations to Kara for being awarded a 2019 Faculty of Health Sciences Graduate Programs Excellence Award!
Updated August 6, 2019: Congratulations to Kara for also winning an Ontario Graduate Scholarship!
During McMaster Spring Mid-Term Recess (February 18-24), the McArthur lab is pleased to present a series of lectures, demonstrations, and training sessions for the Comprehensive Antibiotic Resistance Database (card.mcmaster.ca) and its associated Resistance Gene Identifier (RGI) software, sponsored by the Michael G. DeGroote Institute for Infectious Disease Research (IIDR).
Questions? Email email@example.com
Workshop & Lecture material will be available here: https://github.com/arpcard/state-of-the-card-2019
Antimicrobial Resistance: Emergence, Transmission, and Ecology (ARETE). R. Beiko (PI; Dalhousie University), F. Brinkman (co-PI, Simon Fraser University), A.G. McArthur (co-Applicant) + 4 additional co-Applicants. Genome Canada Bioinformatics and Computational Biology Competition.
Bioinformatics Tools to Improve Data Sharing and Re-use in Public Health – applications in antimicrobial resistance profiling and source tracking. W. Hsiao (PI; University of British Columbia), A.G. McArthur (co-Applicant) + 8 additional co-Applicants. CIHR Project Grant.
Some invitations are more special than others. Dr. Peixoto da Cruz and I went to graduate school together in British Columbia (a long time ago!) and while we have since lived in different hemispheres, the bond remains strong. It was great to visit PUG Goiás and learn about Peixoto’s impressive training program in genetic screening and counselling, plus talk about our AMR surveillance efforts.
Bioinformatics of antimicrobial resistance in the age of molecular epidemiology. Invited Keynote presentation by A.G. McArthur at Reunião de Citogenética do Brasil Central & XII Workshop de Genética da PUC Goiás, Goiânia, Brazil, October 2018.
- 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.