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.
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.
Authors: Graham CF, Glenn TC, McArthur AG, Boreham DR, Kieran T, Lance S, Manzon RG, Martino JA, Pierson T, Rogers SM, Wilson JY, Somers CM. Mol Ecol Resour. 2015 Nov;15(6):1304-15.
Degraded DNA from suboptimal field sampling is common in molecular ecology. However, its impact on techniques that use restriction site associated next-generation DNA sequencing (RADSeq, GBS) is unknown. We experimentally examined the effects of in situ DNA degradation on data generation for a modified double-digest RADSeq approach (3RAD). We generated libraries using genomic DNA serially extracted from the muscle tissue of 8 individual lake whitefish (Coregonus clupeaformis) following 0-, 12-, 48- and 96-h incubation at room temperature posteuthanasia. This treatment of the tissue resulted in input DNA that ranged in quality from nearly intact to highly sheared. All samples were sequenced as a multiplexed pool on an Illumina MiSeq. Libraries created from low to moderately degraded DNA (12-48 h) performed well. In contrast, the number of RADtags per individual, number of variable sites, and percentage of identical RADtags retained were all dramatically reduced when libraries were made using highly degraded DNA (96-h group). This reduction in performance was largely due to a significant and unexpected loss of raw reads as a result of poor quality scores. Our findings remained consistent after changes in restriction enzymes, modified fold coverage values (2- to 16-fold), and additional read-length trimming. We conclude that starting DNA quality is an important consideration for RADSeq; however, the approach remains robust until genomic DNA is extensively degraded.
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.