November 7, 2018; Hamilton, Ontario, Canada; The 9th annual McMaster Innovation Showcase, with focus on biomedical technologies, highlights the role and impact of McMaster research in shaping the future of healthcare. Hosted by the McMaster Industry Liaison Office (MILO) – supporting innovation, commercialization and community engagement. Photo by Ron Scheffler for McMaster University.

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!

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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).

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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.

 

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  • 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.
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Dr. McArthur has been busy doing some Government outreach. In early 2018 he was a Panellist on Artificial Intelligence in Healthcare at Norwegian Health Ministry & Government of Canada Round Table hosted by Hamilton Health Sciences and then in May 2018 represented McMaster University at Research Canada’s Health Research Caucus – Reshaping Health Research and Innovation: Artificial Intelligence and Machine Learning at Parliament Hill, Ottawa.

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Florescu, A., B. Alcock, A. Raphenya, & A.G. McArthur. 2018. Incorporating phenotypic testing into ontological data sharing paradigms. Presentation at Ontario Biology Day, Waterloo, Ontario, Canada.

Lau, T. & A.G. McArthur. 2018. The optimization of antimicrobial resistance surveillance tools. Poster presentation at Ontario Biology Day, Waterloo, Ontario, Canada.

Singh, B., A.G. McArthur, & J. Stone. 2018. Ontological classification of resistance gene annotations for antimicrobial surveillance. Presentation at Ontario Biology Day, Waterloo, Ontario, Canada.

Jenny, M.J., K. Srinivasan, B. O’Shields, & A.G. McArthur. 2018. Sex and age-related differences in cadmium-induced global transcriptomic profiles in adult zebrafish eye tissues. Presentation at the Society of Toxicology 57th Annual Meeting, San Antonio, Texas.

Srinivasan, K., M.J. Jenny, & A.G. McArthur. 2018. Global changes in gene expression in human lens epithelial cells in response to cadmium exposure. Poster presentation at the Society of Toxicology 57th Annual Meeting, San Antonio, Texas

Lau, T. & A.G. McArthur. 2018. The optimization of antimicrobial resistance surveillance tools. Presentation at McMaster Women in Science and Engineering (WISE) Current Research in Engineering, Science & Technology (CREST) Meeting, Hamilton, Ontario, Canada.

Singh, B., A.G. McArthur, & J. Stone. 2018. Ontological classification of resistance gene annotations for antimicrobial surveillance. Presentation at McMaster Women in Science and Engineering (WISE) Current Research in Engineering, Science & Technology (CREST) Meeting, Hamilton, Ontario, Canada.

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Tsang, K.K. & A.G. McArthur. 2017. Encoding the efflux pump phenomena. Oral presentation at the Second American Society for Microbiology Meeting on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatics Pipelines, Washington, D.C.

Background: Efflux pumps are a major mechanism for intrinsic and acquired resistance to our current antibiotic armamentarium. Efflux mechanisms interplay synergistically with other resistance mechanisms, including drug permeability, degradation and inactivation, to strengthen pathogen antimicrobial resistance levels. Despite their clinical relevance, there is no resource that seeks to understand and predict the contribution of efflux pumps in antimicrobial resistance from genome sequence. This has resulted in limited prediction of the full potential of all resistance determinants in a bacterial cell.

Methods: The Comprehensive Antibiotic Resistance Database (CARD, https://card.mcmaster.ca/) and Resistance Gene Identifier (RGI) were optimized for E. coli and P. aeruginosa efflux pump detection through extensive curation and algorithmic development. Literature was mined and analyzed to curate all published information on E. coli and P. aeruginosa efflux pumps into CARD. Algorithmic development of RGI involved creating bioinformatics detection models and refining their parameters. The Efflux Pump Identifier (EPI) was developed to predict efflux pumps and antimicrobial resistance based on RGI results generated using CARD and tested using genome sequences of characterized, clinical multi-drug resistant E. coli and P. aeruginosa isolates.

Results: The Efflux Pump Identifier (EPI) analyzed 124 E. coli and 94 P. aeruginosa clinical multi-drug resistant samples to predict efflux pumps and their complex regulatory networks under three paradigms: 1) Perfect, 2) Partial, and 3) Putative. The Perfect paradigm identifies perfect matches to known efflux pumps curated into CARD. The Partial algorithm detects efflux pumps where at least one or more components of the efflux pump is not a perfect match to an efflux pump component in CARD, but likely a functional homolog. Lastly, the Putative algorithm discovers potential efflux pumps where all components are not perfect matches to previously curated components in CARD.

Conclusions: The development of the Efflux Pump Identifier (EPI) devotes effort to an area in antimicrobial resistance where insufficient attention has been paid in the past. This is a step towards answering the long-standing question in the efflux pump phenomena; is the detected efflux pump genotype being expressed to present a specific phenotype? Using the Efflux Pump Identifier (EPI) in tandem with the existing repertoire of detection tools for dedicated and mutational resistance determinants leads to the complete prediction of antibiogram from genome sequence.

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We haven’t been travelling much this year, but our collaborators have been busy!

Dearborn, D.C., A.B. Gager, A.G. McArthur, M.E. Gilmour, E. Mandzhukova, R.A. Mauck. 2017. How to get diverse MHC genotypes without disassortative mating. Presentation at the 2017 Annual Meeting of the Society for Integrative and Comparative Biology, New Orleans, Louisiana.

McLean, M., D. Theriault, M. Kelley, B.A. Lago, A.G. McArthur, & L. Williams. 2017. Role of Nfe2 and pro-oxidant exposure in inner ear development in zebrafish. Presentation at the Society of Toxicology 56rd Annual Meeting, Baltimore, Maryland.

Williams, L.M., B.A. Lago, A.G. McArthur, A.R. Raphenya, N. Pray, N. Saleem, S. Salas, K. Paulson, R.S. Mangar, Y. Liu, A.H. Vo, & J.A. Shavit. 2017. The transcription factor, Nuclear factor, erythroid 2 (Nfe2), is a regulator of the oxidative stress response during Danio rerio development. Presentation at the Society of Toxicology 56rd Annual Meeting, Baltimore, Maryland.

Winsor, G.L., C. Bertelli, K.K. Tsang, B. Alcock, A.G. McArthur, & F.S.L. Brinkman. 2017. Pseudomonas Genome Database 2017: Improved gene/AMR/VF/genomic island annotations, comparative genome analyses, and a platform for facilitating public health genomic epidemiology. Presentation at the 16th International Conference on Pseudomonas, Liverpool, United Kingdom.

 

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arjun-at-iidr-trainee-daykirill-usra-poster

Three of our undergraduate students gave poster presentations in the last month. Arjun Sharma (Biochemistry & Biomedical Sciences 3rd year) outlined his work on developing the Comprehensive Antibiotic Resistance Database’s (arpcard.mcmaster.ca) CARD*Shark text mining algorithms at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day while Kirill Pankov (Biomedical Discovery & Commercialization 4th year) presented the results of his summer NSERC Undergraduate Student Research Award (USRA) research in the Laboratory of Dr. Joanna Wilson into the origin of Cnidarian P450 enzymes, work he is continuing in our lab as part of his thesis research. Mohammad Khan (Biomedical Discovery & Commercialization 4th year), a thesis student in the Laboratory of Dr. Eric Brown that collaborates with our group, also presented a poster at IIDR Trainee Day on his work on chemical-genetic interaction database design.

Sharma, A.N., S. Doshi, A.R. Raphenya, B. Alcock, B.M. Dave, B.A. Lago, K.K. Tsang, & A.G. McArthur. 2016. CARDShark: Computer-assisted biocuration of the Comprehensive Antibiotic Resistance Database. Poster presentation at the 2016 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.

Pankov, K., A.G. McArthur & J.Y. Wilson. 2016. The Cytochrome P450 (CYP) superfamily in the Cnidarian phylum. Poster presentation at the 2016 Undergraduate Student Research Awards (USRA) Poster Session, Hamilton, Ontario, Canada.

Khan, M.A., S. French, B. Aubie, A.G McArthur & E.D. Brown. 2016. Challenging common screening filters through analysis of a chemical-genetic screening database. Poster presentation at the 2016 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.

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