Maguire, F., B. Alcock, A.R. Raphenya, B. Jia. E.J. Griffiths, T.C. Matthews, J. Adam, A. Petkau, G.L. Winsor, IRIDA Consortium, R.G. Beiko, F.S.L. Brinkman, W.W.L. Hsiao. G. Van Domselaar, A.G. McArthur. 2019. Integrated Rapid Infectious Disease Analysis: A comprehensive platform for public health bioinformatics and AMR surveillance using genomic data. Poster presentation at the Canadian Society of Microbiologists Annual Meeting, Sherbrooke, Quebec.
Maguire, F., B. Jia, B. Alcock, A.R. Raphenya, F.S.L. Brinkman, A.G. McArthur, & R.G. Beiko. 2019. Precise identification of antimicrobial resistance determinants from metagenomic data. Oral presentation at the Canadian Society of Microbiologists Annual Meeting, Sherbrooke, Quebec.
Alcock, B.P., A.R. Raphenya, F. Maguire, F.S. Brinkman, R.G. Beiko, & A.G. McArthur. 2019. Resistome and variant prediction for improved antimicrobial surveillance with the Comprehensive Antibiotic Resistance Database. Poster presentation at the American Society for Microbiology Microbe Meeting, San Francisco, California.
Raphenya, A.R., T.T.Y. Lau, B. Alcock, K.K. Tsang, F. Maguire, F.S. Brinkman, R.G. Beiko, & A.G. McArthur. 2019. Resistance Gene Identifier (RGI) – Prediction of antimicrobial resistance genes and mutations for genomic and metagenomic sequencing data. Oral presentation at the American Society for Microbiology Microbe Meeting, San Francisco, California.
Chen, J. C.-Y., C.G. Clark, A. Bharat, A.G. McArthur, M.R. Graham, G.R. Westmacott, & G. Van Domselaar. 2019. Detection of antimicrobial resistance using proteomics and the Comprehensive Antibiotic Resistance Database: A case study. Presentation at 27th Conference on Intelligent Systems in Molecular Biology & 18th European Conference on Computational Biology, Basel, Switzerland.
Tsang, K.K., F. Maguire, H. Zubyk, S. Chou, G.D. Wright, R.G. Beiko, & A.G. McArthur. 2019. Combining multiple features and algorithms to learn antimicrobial resistance genotype-phenotype relationships. Poster presentation at 27th Conference on Intelligent Systems in Molecular Biology & 18th European Conference on Computational Biology, Basel, Switzerland.
Griffiths, E., D. Dooley, G. Gosal, I. Gill, S. Russell, L. Tindale, V. Pichler, T. Matthews, A. Petkau, J. Adam, D. Fornika, G. Winsor, F. Maguire, B. Alcock, The IRIDA Consortium, A.G. McArthur, R. Beiko, M. Graham, F. Brinkman, G. van Domselaar, & W. Hsiao. 2019. Empowering data sharing for genomics-based public health surveillance using ontologies. Presentation at the 12th International Meeting On Epidemiological Markers (IMMEMXII), Dubrovnik, Croatia.
Matthews, T., F. Bristow, A. Petkau, J. Adam, J. Thiessen, S. Sidhu, P. Kruczkiewicz, D. Dooley, E. Griffiths, D. Fornika, G. Winsor, M. Graham, The IRIDA Consortium, A.G. McArthur, E. Taboada, R. Beiko, F. Brinkman, W. Hsiao, & Gary van Domselaar. 2019. Canada’s Integrated Rapid Infectious Disease Analysis Platform (IRIDA). Presentation at the 12th International Meeting On Epidemiological Markers (IMMEMXII), Dubrovnik, Croatia.
Day, E.A., R.J. Ford, B.K. Smith, P. Mohammadi-Shemirani, M.R. Morrow, R.M. Gutgesel, R. Lu, A.R. Raphenya, A.G. McArthur, N. McInnes, G. Paré, H.C. Gerstein, & G.R. Steinberg. 2019. GDF15 is a metformin stimulated hepatokine that is important for promoting weight loss. Presentation at the Lunenfeld-Tanenbaum International Symposium: Translational Diabetes and Metabolism Research Day, Toronto, Ontario.
Griffiths, E., T. Matthews, A. Petkau, J. Adam, D. Dooley, D. Fornika, G. Winsor, F. Maguire, B. Alcock, The IRIDA Consortium, A.G. McArthur, R. Beiko, M. Graham, F. Brinkman, G. van Domselaar, & W. Hsiao. 2019. Empowering local to global WGS-based surveillance and investigation: The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform. Presentation at the Meeting on Global Microbial Identifier, Singapore.
Matthews, T., J. Adam, A. Petkau, F. Maguire, B. Alcock, A.R. Raphenya, E.J. Griffiths, D. Dooley, B. Jia, G.L. Winsor, The IRIDA Consortium, R.G. Beiko, A.G. McArthur, F.S.L. Brinkman, G.L. Van Domselaar, & W.W.L. Hsiao. 2019. Integrated Rapid Infectious Disease Analysis (IRIDA): a comprehensive and distributed platform for public health genomic epidemiology. Poster presentation at the 2019 Applied Bioinformatics and Public Health Microbiology (ABPHM) Meeting, Cambridge, United Kingdom.
Petkau, A., T. Matthews, F. Bristow, J. Adam, J. Thiessen, S. Sidhu, P. Kruczkiewicz, E. Griffiths, D. Dooley, D. Fornika, G. Winsor, M. Graham, A.R. Raphenya, The IRIDA consortium, E. Taboada, A.G. McArthur, R. Beiko, W. Hsiao, F. Brinkman, G. Van Domselaar. 2019. The IRIDA Platform for Microbial Genomics. Oral presentation at the 2019 Galaxy Community Conference, Freiburg, Germany.
Porter, A.F., A.T. Duggan, J. Klunk, E.C. Holmes, H. Poinar, A.N. Dhody, R. Hicks, G. Smith, M. Humpherys, A. McCollum, W. Davidson, K. Wilkins, Y. Li, A. Burke, H. Polasky, L. Flanders, D. Poinar, A.R. Raphenya, B. Alcock, T.T. Lau, A.G. McArthur, & B. Golding. 2019. Unraveling the evolutionary history of the vaccinia virus, the vaccine for smallpox. Presentation at the Annual Meeting of the Society for Molecular Biology and Evolution, Manchester Central, United Kingdom.
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!
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.
Congratulations to Rachel Tran on winning a 2019 DBCAD Summer Fellowship! These competitive awards are designed to support students working in the labs of members from both the David Braley Centre for Antibiotic Discovery and Michael G. DeGroote Institute for Infectious Disease Research during their summer practicum. A full list of awardees can be found here. Learn more about Rachel’s work at Ontario Biology Day 2019:
Tran, H.K.R., S. Ahmad, J.C. Whitney, & A.G. McArthur. 2019. Expanding the Virulence Ontology (VIRO) to determine the evolution of a secretion system effector. Presentation at Ontario Biology Day, London, Ontario, Canada.
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
A busy year for Dr. McArthur outside of academia…
- Integrated Health Biosystems Chair – Combatting Antimicrobial Resistance in the Age of Molecular Epidemiology. Invited presentation at the Cisco Research Chairs Summit, Toronto.
- The Comprehensive Antibiotic Resistance Database (CARD) and the Resistance Gene Identifier – prediction of antimicrobial resistance genes for genomic and metagenomic sequencing data. Invited presentation at the Integrated Rapid Infectious Disease Analysis (IRIDA) Annual General Meeting, Winnipeg, Manitoba.
- The Comprehensive Antibiotic Resistance Database. Invited presentation at the Agriculture & Agri-Food Canada and Canadian Food Inspection Agency Genomics Research and Development Initiative: GRDI-AMR Annual General Meeting.
- Facilitator & Speaker, Artificial Intelligence: The Art of the Possible, Niagara Health Board of Directors Retreat (Niagara-on-the-Lake, Ontario).
- Represented McMaster University at McMaster – Queen’s Park Government Reception, Gardiner Museum, Toronto.
- Represented McMaster University at Research Canada’s Health Research Caucus – Reshaping Health Research and Innovation: Artificial Intelligence and Machine Learning (Parliament Hill, Ottawa, Ontario).
- Panellist – Artificial Intelligence in Healthcare, Norwegian Health Ministry & Government of Canada Round Table hosted by Hamilton Health Sciences (Hamilton, Ontario).
Congratulations to #TeamVirulence for winning the 2018 McMaster Innovation Showcase People’s Choice Poster Award for their poster entitled, “Examining the relationship between virulence and antimicrobial resistance via expansion of the Comprehensive Antibiotic Resistance Database (CARD)”! Left to right: Anatoly MiroshnichenkoHiu-Ki Rachel Tran, Sally Yue Min, and Rafik El Werfalli.
#TeamVirulence also presented their work at the 2018 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day!
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.
Maguire, F., B. Alcock, F.S. Brinkman, A.G. McArthur, & R.G. Beiko. 2018. AMRtime: Rapid Accurate Identification of Antimicrobial Resistance Determinants from Metagenomic Data. Oral presentation at the Third American Society for Microbiology Meeting on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatics Pipelines, Washington, D.C.
Abstract: Metagenomics, the direct sequencing of the mixture of genomes present in a sample, is an increasingly common workflow within the life sciences. It is frequently used to investigate previously intractable problems such as the functional characterisation of entire microbial environments. One such use-case of global and national public-health importance is analysing the nature and transmission dynamics of antimicrobial resistance (AMR) determinants in human, agri-food and environmental samples. Recently some tools have been developed to profile AMR from metagenomes, however, these are generally limited to profiling at the level of AMR genes clustered by % sequence identity, which may or may not be biologically meaningful. By exploiting the expertly curated ontological structure of the Comprehensive Antibiotic Resistance Database (CARD) and new CARD Prevalence datasets, we have developed an approach using a hierarchical set of machine learning classifiers. This allows us to produce gene-specific AMR profiles to 2386 determinants as well as profiles for higher order, biologically informed, AMR gene family groups. Firstly, DIAMOND based heuristically accelerated homology searches are used to filter out non-AMR related metagenomic reads. This filtering has been optimised to prioritise minimisation of false negatives over minimising false positives. Features generated from these homology searches as well as sequence features are then used to train a random forest classifier to classify filtered reads into one of 227 CARD AMR gene families (e.g. MCR phosphoethanolamine transferase). For each gene family an additional random forest classifier is trained to classify reads into one of the specific AMR determinants belonging to that family (e.g. MCR-1, MCR-2, MCR-3 etc.). This process involves very little computational overhead when classifying beyond the initial homology search. On a fully held out test-set of MiSeq reads simulated from the CARD canonical gene sequences this method resulted in an average precision and recall of 0.993 and 0.987 at the AMR gene family level. Within the 227 AMR families, 70% (158) had an average F1-score greater than 0.99 for classification to specific AMR determinants. A further 10% (24) averaged F1-scores between 0.8 and 0.99. In comparative analyses on the same dataset this outperformed homology searches alone, read mapping and variation graph based methods in terms of average overall accuracy and precision. Further work will aim to improve classification within certain families and expand AMRtime to include variant based AMR models as well as meta-models (e.g. multi-component efflux pump systems).