IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. We now report a major new release of IslandViewer, since the last release in 2013. IslandViewer 3 incorporates a completely new genome visualization tool, IslandPlot, enabling for the first time interactive genome analysis and gene search capabilities using synchronized circular, horizontal and vertical genome views. In addition, more curated virulence factors and antimicrobial resistance genes have been incorporated, and homologs of these genes identified in closely related genomes using strict filters. Pathogen-associated genes have been re-calculated for all pre-computed complete genomes. For user-uploaded genomes to be analysed, IslandViewer 3 can also now handle incomplete genomes, with an improved queuing system on compute nodes to handle user demand. Overall, IslandViewer 3 represents a significant new version of this GI analysis software, with features that may make it more broadly useful for general microbial genome analysis and visualization.
Amos Raphenya and Pearl Guo have joined the McArthur Lab! Amos graduated from McMaster with Bachelor of Computer Engineering in 2008 and joins the lab as a core software engineer, for both our drug resistance and ecotoxicogenomics projects. Pearl just finished her second year at the University of Waterloo’s Computer Science co-op program, with a minor in Bioinformatics. Pearl will be performing a 3 month co-op position in the lab, with a focus on algorithms for prediction of glycopeptide resistance.
One of the themes in the McArthurLab is research at the intersection of academia, government, and industry. We endeavour to work with government agencies and industrial partners as much as with fellow academics. This is in part a reflection of our emphasis upon applied research but also my history of starting and owning my own bioinformatics company between being a professor in the United States (until 2006) and starting my faculty position at McMaster (in late 2014). This weekend I had the opportunity to participate in @Mac_Spectrum’s Summer Startup where student teams worked to create a start-up company in 36 hours. I got to meet with a lot of the teams and discuss their start-up strategies as well as give a talk on “Software in Science Entrepreneurship” where I discussed our efforts to partner our antibiotic resistance software with government and industry partners. It was a great weekend and impressive competition. Congratulations to Clear Roots, Avaro, and E-Dopa on their awards!
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.
Dr. Nancy Hopkins’ article is a worthy read. Hopkins, Nancy. Reflecting on Fifty Years of Progress for Women in Science. DNA and Cell Biology. March 2015, 34(3): 159-161.
The McArthur Lab welcomes Tariq Elsayegh from the Royal College of Surgeons in Ireland, where he is in the six year medical program. Tariq is undertaking a 6 week project to build bioinformatics models for colistin resistance in the Comprehensive Antibiotic Resistance Database.
The major histocompatibility complex (Mhc) is subject to pathogen-mediated balancing selection and can link natural selection with mate choice. We characterized two Mhc class II B loci in Leach’s storm-petrel, Oceanodroma leucorhoa, focusing on exon 2 which encodes the portion of the protein that binds pathogen peptides. We amplified and sequenced exon 2 with locus-specific nested PCR and Illumina MiSeq using individually barcoded primers. Repeat genotyping of 78 single-locus genotypes produced identical results in 77 cases (98.7 %). Sequencing of messenger RNA (mRNA) from three birds confirmed expression of both loci, consistent with the observed absence of stop codons or frameshifts in all alleles. In 48 birds, we found 9 and 12 alleles at the two loci, respectively, and all 21 alleles translated to unique amino acid sequences. Unlike many studies of duplicated Mhc genes, alleles of the two loci clustered into monophyletic groups. Consistent with this phylogenetic result, interlocus gene conversion appears to have affected only two short fragments of the exon. As predicted under a paradigm of pathogen-mediated selection, comparison of synonymous and non-synonymous substitution rates found evidence of a history of positive selection at putative peptide binding sites. Overall, the results suggest that the gene duplication event leading to these two loci is not recent and that point mutations and positive selection on the peptide binding sites may be the predominant forces acting on these genes. Characterization of these loci sets the stage for population-level work on the evolutionary ecology of Mhc in this species.
Justin Jia (left) and Daim Sardar (right) have joined the McArthur Lab. Justin has joined as a 8 month McMaster Honours Biochemistry – Biomedical Research Specialization Co-Op placement and will be working as a Comprehensive Antibiotic Resistance Database biocurator. Daim will be in the lab 8 weeks performing an Independent Project on 16S rRNA mutations conferring antibiotic resistance as part of his Honours Integrated Science Program (iSci) 3A12 coursework.