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!

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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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April 2019 big #card_release #RGI5! Resistance Gene Identifier Version 5: entirely new algorithms for metagenomics data, new options for genomes & assemblies. Extensively updated documentation, http://github.com/arpcard/rgi

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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.

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A week of lectures, demos, and training for the Comprehensive Antibiotic Resistance Database

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 card@mcmaster.ca

  


Workshop & Lecture material will be available herehttps://github.com/arpcard/state-of-the-card-2019


 

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Dr. David Speicher has joined the McArthur Lab as our new Molecular Epidemiology Postdoctoral Fellow! David joins us via clinical epidemiology research in infectious disease at St. Joseph’s Healthcare Hamilton plus extensive training and experience in Cambodia, India, Sri Lanka, Kenya, and Australia. David has a depth of experience in infectious disease, virology, molecular biology, epidemiology and biostatistics, microbiology, and diagnostic techniques and will be leading infectious disease molecular epidemiology collaborations with McMaster Children’s Hospital, St. Joseph’s Healthcare Hamilton, and Hamilton Health Sciences with an emphasis on antimicrobial resistance, C. difficile, H. pylori, ShigellaChlamydia trachomatis, and Mycoplasma genitalium. Hear Dr. Speicher talk about his research program on CFMU radio.

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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.

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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 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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