Building upon her successful Biochem 3A03 project, Tammy Lau is staying in the lab for 2017-2018 as part of her Biochem 4T15 Research Thesis. Tammy’s research will be focussed on developing new classification and visualization tools for our Resistance Gene Identifier (RGI), plus extending the RGI towards k-mer approaches for predicting pathogen-of-origin for metagenomics antimicrobial resistance gene sequences.
Antibiotic resistance is ancient and widespread in environmental bacteria. These are therefore reservoirs of resistance elements and reflective of the natural history of antibiotics and resistance. In a previous study, we discovered that multi-drug resistance is common in bacteria isolated from Lechuguilla Cave, an underground ecosystem that has been isolated from the surface for over 4 Myr. Here we use whole-genome sequencing, functional genomics and biochemical assays to reveal the intrinsic resistome of Paenibacillus sp. LC231, a cave bacterial isolate that is resistant to most clinically used antibiotics. We systematically link resistance phenotype to genotype and in doing so, identify 18 chromosomal resistance elements, including five determinants without characterized homologues and three mechanisms not previously shown to be involved in antibiotic resistance. A resistome comparison across related surface Paenibacillus affirms the conservation of resistance over millions of years and establishes the longevity of these genes in this genus.
See more: McMaster Daily News
A 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.
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.
Authors: Freschi et al. Front Microbiol. 2015 Sep 29;6:1036.
The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aeruginosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P. aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care.