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.
Wright, G.D. & A.G. McArthur. 2015. A bioinformatic platform for the characterization of antibiotic resistance in bacterial genomes and metagenomes. Presentation at the 2015 Interscience Conference of Antimicrobial Agents and Chemotherapy, San Diego, California.
The increasingly routine sequencing of bacterial genomes in biomedical research and the clinical lab requires access to easy to use, efficient, and accurate bioinformatic tools for analysis of bacterial traits from virulence to drug resistance. To contribute to this growing need, we have developed a platform for the investigation of antibiotic resistance elements, the Comprehensive Antibiotic Resistance Database (http://arpcard.mcmaster.ca/). This resource includes a manually curated database of over 3000 resistance genes and associated literature, protein structures, and target antibiotics. Associated with this platform are tools to aid in the study of resistance including the Resistance Gene Identifier (RGI) that can analyze genomic data for the presence of resistance elements. Our goal is to accurately predict resistance phenotype from genomic data. Our analysis of many genomes and associated antibiograms reveals a reservoir of ‘silent’ resistance genes that are predicted to encode viable resistance elements yet the phenotype is drug sensitive. Our efforts to manage these issues along with identifying and adding new resistance genes will be presented.
Authors: McArthur AG, Wright GD. Curr Opin Microbiol. 2015 Jul 31;27:45-50.
Antimicrobial resistance is a global health challenge and has an evolutionary trajectory ranging from proto-resistance in the environment to untreatable clinical pathogens. Resistance is not static, as pathogenic strains can move among patient populations and individual resistance genes can move among pathogens. Effective treatment of resistant infections, antimicrobial stewardship, and new drug discovery increasingly rely upon genotype information, powered by decreasing costs of DNA sequencing. These new approaches will require advances in microbial informatics, particularly in development of reference databases of molecular determinants such as our Comprehensive Antibiotic Resistance Database and clinical metadata, new algorithms for prediction of resistome and resistance phenotype from genotype, and new protocols for global collection and sharing of high-throughput molecular epidemiology data.
McArthur, A.G., Waglechner, N., Nizam, F., Pereira, S.K., Jia, B., Sardar, D., Westman, E.L., Pawlowski, A.C., Johnson, T., Lo, R., Courtot, M., Brinkman, F.S., Williams, L.E., Frye, J.G., & Wright, G.D. 2015. The Comprehensive Antibiotic Resistance Database. Presentation at the 4th ASM Conference on Antimicrobial Resistance in Zoonotic Bacteria and Foodborne Pathogens, Washington, District of Columbia.
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. Rather, phenotype based criteria dominate the epidemiology of antibiotic action and effectiveness. As a result, there is a poor understanding of which antibiotic resistance genes are in circulation, which ones are 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 (arpcard.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 Probabilistic Graphic Models for improved AMR phenotype prediction, and development of portable command-line genome analysis tools.
Williams, L.E., A.G. McArthur, N. Waglechner, F. Nizam, P.T. Desai, M. McClelland, G.M. Weinstock, J.B. Barrett, L.M. Hiott, C.R. Jackson, & J.G. Frye. 2014. Genetic variation and genomic context of antibiotic resistance genes and mobile genetic elements in Salmonella from non-human animals. Presentation at the 114th General Meeting of the American Society for Microbiology, Boston, Massachusetts.
Zittermann, S.I., A.G. McArthur, N.V. Fittipaldi, V. Braun, L. Vrbova, D. Middleton, G. Mallo, R. Ahmed, P. Huk, M. Lombos, V.G. Allen. 2014. Whole genome sequencing of Salmonella Enteritidis for public health investigation. Presentation at the 114th General Meeting of the American Society for Microbiology, Boston, Massachusetts.
Objective: To understand the spread of drug resistance and identifying diagnostic probes among the local tuberculosis (TB) strains in order to design rational control tools for tuberculosis controls. Methods: TA cloning and sequencing were used to characterize mutation associated with RIF resistance in 69 bp region of the gene, rpoB. The analysis identified two regions of mutations but no unusual insertion and deletion. No mutation was observed in RIF sensitive strains. Results: We employed Random Amplified Polymorphic DNA (RAPD) analysis for typing strains of M. tuberculosis to determine whether new strains were present among M. tuberculosis isolates circulating in Yaounde. Three groups (I to III) of M. tuberculosis were identified among 93 isolates randomly selected. RAPD analysis provided a rapid and easy means of identifying polymorphism in M. tuberculosis isolates, and it was found to be a valuable alternative epidemiological tool. RAPD was used to select the new site of diagnostic by PCR. Also single nucleotide polymorphisms between M. tuberculosis and M. bovis were found, suggesting that RAPD can be a useful technique for distinguishing between species. Conclusions: Molecular typing is defined as the integration of conventional epidemiological approach to track specific strains of pathogens in order to understand the distribution of disease in populations.