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.
Speicher, D.J., K. Luinstra, J. Maciejewski, K.K. Tsang, A.G. McArthur, & M. Smieja. 2019. Clostridioides difficile strain divergence over time. Oral presentation at the Association of Medical Microbiology and Infectious Disease Canada (AMMI Canada) & Canadian Association for Clinical Microbiology and Infectious Diseases (CACMID) Joint Annual Conference, Ottawa, Ontario.
Background: Clostridioides difficileinfection (CDI) is a serious hospital-associated infection with severe outbreaks caused by the hypervirulent NAP1/MLST-1 strain. Whole genome sequencing has shown that most outbreak strains are clonal whereas non-outbreaks display a wide diversity of strains. To examine strain diversity in clinical settings, a subset of C. difficileisolates from symptomatic CDI from an acute care hospital were compared to isolates from C. difficilecolonized (CDC) asymptomatic subjects from the same hospital.
Methods: A subset of PCR-positive stool samples from clinically confirmed CDI isolates from 2016 (13/110), 2017 (8/111), and 2018 (13/65), and CDC from 2017 (17/185) were cultured 3-times consecutively on CHROMagar™ C. difficile, sub-cultured on Columbia colistin-nalidixic acid (CNA) media, had DNA isolated, shotgun sequenced, and genome assembled for both MLST typing and genome-wide SNP phylogenetic analysis.
Results: Based on MLST profiles, the C. difficiletypes detected were diverse. Of the presumed binary toxin positive/NAP1 strains (i.e. PCR tcdA/tcdBpositive) 7/12 (58%) were NAP1/MLST-1 and 3/12 (25%) were NAP7/MLST-11. NAP1/MLST-1 was not detected in any CDC isolate. NAP4/MLST-2,14 were detected in 2016 (n=4), 2017 (n=2), 2018 (n=1), and in CDC isolates (n=3). MLST-42 was dominant in CDC isolates (5/17; 29%) and decreased in prevalence in CDI isolates over time (2016=4; 2017=0; 2018=1).
Conclusion: C. difficilestrains amongst both CDI and CDC individuals are highly divergent. Whilst molecular assays are misclassifying 25% of “NAP1” strains, both NAP1 and NAP7 are hypervirulent. The number of MLST-42 CDC isolates is concerning as it has been reported to be the most common strain causing CDI among U.S. adults. This highlights the need for continued genomic surveillance of both CDI and CDC individuals. Genome-wide SNP phylogenetic analysis is currently being performed.
The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
CARD Curation: Expanded MCR, OXA & IMP beta-lactamase, and macrolide phosphotransferase (MPH) sequence curation. Updated nomenclature for MPHs and drug resistant dihydrofolate reductases (dfr). Updated classification of ADC beta-lactamases.
Ontologies: Addition of 518 draft virulence ontology (VIRO) terms.
Prevalence, Resistomes, & Variants: Expansion to 82 pathogens (more Brucella species), 81,000+ resistomes, and 173,000+ AMR allele sequences based on sequence data acquired from NCBI on 28-Feb-2019, analyzed using RGI 4.2.2 (DIAMOND homolog detection) and CARD 3.0.1.
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 firstname.lastname@example.org
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.
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.
The McArthur lab and the Comprehensive Antibiotic Resistance Database are proud to join the Canadian Anti-Infective Innovation Network, International Genomic Epidemiology Application Ontology Consortium, and Integrated Rapid Infectious Disease Analysis Project!
4th year Bachelor of Health Sciences student Alexandra Florescu has joined us for her Biochem 3A03 (Biochemical Research Practice) course. Alexandra will be collaborating with colleagues in the Genomic Epidemiology Ontology Consortium (genepio.org) on developing ontological terminology for phenotypic tests of antimicrobial resistance and microbial virulence via our ongoing Genome Canada Bioinformatics & Computational Biology funding.
Suman Virdee – Developing a Galaxy based Pipeline for RNA-Seq Analysis in Stem Cell Biology
Kirill Pankov – The Cytochrome P450 (CYP) Superfamily in the Cnidarian Phylum
Jonsson Liu – Clinical virulence detection and Clostridium difficile clonality
Annie Cheng – Predicting Plasmid-Mediated Antimicrobial Resistance from Whole Genome Sequencing
Godwin Chan – Using the Galaxy Platform to Increase Accessibility for Structure Determination via Cryo-Electron Microscopy
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.