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.

Nucleic Acids Research 2019 Oct 29. [Epub ahead of print]

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.

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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.

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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.

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The Comprehensive Antibiotic Resistance Database: Expanded tools for molecular surveillance of antimicrobial resistance (AMR) in the environment, agriculture, and clinic. A.G. McArthur (PI), G. Van Domselaar (co-I, Public Health Agency of Canada), R. Beiko (Co-I, Dalhousie University), F. Brinkman (co-I, Simon Frasier University). CIHR Project Grant.

Preventing Clostridium difficile infections by identifying asymptomatic carriers. D. Mertz, M. Loeb, J. Pernica, S. Khan, M. Smieja, A.G. McArthur (Co-Applicants). Hamilton Health Sciences Research Strategic Initiative Program.

Developing a strain-specific test for rapid diagnosis of Clostridium difficile. Y. Li (PI), A.G. McArthur (co-I), C. Lee (co-I). CIHR Antimicrobial Resistance: Point of Care Diagnostics in Human Health.
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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.

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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.

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Congratulations to this year’s crop of BiomedDC 4A15 thesis students for 8 month research projects well done! From left to right:

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

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genome-canada-1A cross-national research consortia co-led by McMaster’s Andrew McArthur is receiving two of 16 federal grants to further develop a big data solution to the growing problem of antimicrobial resistance (AMR). The government’s investment, totaling more than $4M, is the result of Genome Canada’s 2015 Bioinformatics and Computational Biology Competition, a partnership with the Canadian Institutes of Health Research (CIHR). McArthur and his colleagues will receive $500,000 over two years. McArthur will work closely with researchers from the University of British Columbia, Simon Fraser University, Dalhousie University and the Public Health Agency of Canada to design and develop novel software and database systems that will empower public health agencies and the agri-food sector to rapidly respond to threats posed by infectious disease outbreaks and food-borne illnesses.

Full Coverage: Faculty of Health Sciences, Genome Canada, Newswire, Hamilton Spectator

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McArthur, A.G.B. Jia, A.R. Raphenya, P. Guo, K. Tsang, B. Dave, B. Alcock, B. Lago, N. Waglechner, & G.D. Wright. 2016. The Comprehensive Antibiotic Resistance Database – A Platform for Antimicrobial Resistance Surveillance. Invited presentation at the 2nd Conference Rapid Microbial NGS and Bioinformatics: Translation Into Practice, Hamburg, Germany.

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. Phenotype based criteria dominate the epidemiology of antibiotic action and effectiveness. There is a poor understanding of which antibiotic resistance genes are in circulation, which 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 (card.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 meta-models for improved AMR phenotype prediction, and release of portable command-line genome analysis tools.

 

* presenter underlined, trainees in bold
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