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.
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.
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.
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
Congratulations to Kara Tsang and Zachary Lin on completion of their Biomedical Discovery and Commercialization (BDC) 4A15 thesis research! Both Kara & Zachary presented their research results at the 2016 BDC Engage Symposium.
Zachary Lin: Adapting Galaxy bioinformatics to outbreak- associated Clostridium difficile
Kara Tsang: The translation of biocuration to metagenomic analysis for combatting multi-drug resistant Pseudomonas aeruginosa
Combatting Antibiotic Resistance Using Surveillance – click on the image to watch the 10 minute video. More details here.
The completion of the human genome project in 2001 sparked the beginning of a sequencing revolution with applications that are only now being realized by researchers. The decreasing cost of DNA sequencing has ignited a continuous generation of genomic data with a limited number of researchers able to manipulate the output. Consequentially the demand to examine this genetic information has forced bioinformaticians to improve the analytical tools involved in sequence analysis. Galaxy is a user-friendly analytical platform where researchers without a computational background can navigate their way through an investigation and use various analytical tools and workflows to assist them with their genomic research (1). Galaxy enables the addition of novel software into the environment by individual users to fill in the gaps of tools that haven’t been created by the Galaxy team. This project will focus on a particular analytical gap concerning tools related to antibiotic resistance, phylogenetics, and bacterial virulence. Currently, the proposed software to be adapted to the galaxy setting includes a resistance gene identifier (RGI) associated with the comprehensive antibiotic resistance database (CARD) (2), a single nucleotide polymorphism identifier (BANSP) , and novel virulence factor identification software associated with the virulence factor database (VFDB) (3). The combination of Galaxy’s existing ToolShed and these unique additions will create a comprehensive analytical environment that can be applied to realistic situations. One such situation that this project will concentrate on refers specifically to the outbreaks of Clostridium difficile (C. diff) in the health care system.
The loss of effective antimicrobials is reducing the ability to protect the global population from infectious diseases, leading to profound impacts on the healthcare system, international trade, agriculture, and environment. The field of antibiotic drug discovery and the monitoring of the dynamic and new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. The curation and directed development of the Comprehensive Antibiotic Database (CARD) will advance the understanding of the genetics, genomics, and threat severity of antibiotic resistance, while simultaneously improving its ability to accurately predict and screen for antibiotic resistance genes within raw genomes. Strategically advancing the Antibiotic Resistance Ontology (ARO), the unique organizing principle of the CARD, allows the value of big data in disparate realms of research to be used and understood by the multidisciplinary efforts working to combat the emergence and prevalence of the ESKAPE pathogens, a critical driving force of the global health crisis.
Dr. McArthur gave a MacTalk at McMaster’s Big Ideas Better Cities evenings on Health and Social Innovation through Big Data about “Combatting antibiotic resistance using surveillance”. See the related How ‘Big Data’ can help solve big problems article at McMaster Daily News and the coverage at the Hamilton Spectator.