The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca

CARD Curation: Addition of HERA, TRU, & ACI beta-lactamases, sul4, and new quinolone efflux pumps.

Antibiotic Resistance Ontology: Expanded to include an entirely new branch describing AMR phenotypic testing methods. ARO additionally now officially available at the OBO Foundry, allowing formal integration with other ontological resources, most notably the Genomic Epidemiology Application Ontology (GenEpiO), https://github.com/genepio/genepio.

Resistance Gene Identifier: Resistome prediction for low quality or low coverage assemblies, merged metagenomics reads, and small plasmids or assembly contigs. Includes prediction of partial AMR genes. Support added for Docker operating-system-level virtualization (i.e. containerization).

Prevalence, Resistomes, & Variants: Expanded to 67 important pathogens, with a focus on ESKAPEs, WHO Priority Pathogens, and agents of sepsis.

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The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca

This February 2018 release is our largest to date and includes new data types, a new classification system, an entirely new version of the Resistance Gene Identifier, and website improvements.

CARD Curation: 37 new ADC beta-lactamases, 21 PDC beta-lactamases, new MCR proteins, 23 rRNA mutations, resistant isoleucyl-tRNA synthetases, hundreds of new resistance mutations, and more. While in past releases all curated AMR mutations were those characterized from clinical isolates, CARD now additionally includes mutations discovered via in vitro selection experiments. Ontological improvements have been made to enable an entirely new classification system for CARD data and RGI results: resistance determinants are now systematically categorized by AMR Gene Family, Drug Class, and Resistance Mechanism. The Antibiotic Resistance Ontology is now additionally available via GitHub, https://github.com/arpcard.

Resistance Gene Identifier: Entirely new codebase, compatible with CARD data (card.json) version 2.0.0 and up (download separately). Open Reading Frame (ORF) prediction using Prodigal, homolog detection using BLAST (default) or DIAMOND, and Strict significance based on CARD curated bitscore cut-offs. Addition of rRNA mutation and efflux over-expression models. Hits of 95% identity or better are automatically listed as Strict. All results organized by revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Revised documentation, command line menu, and website graphical interface. The Resistance Gene Identifier is now additionally available via GitHub, https://github.com/arpcard.

Prevalence, Genomes, & Variants: Expansion of our computer-generated data set on the prevalence of AMR genes and variants among the sequenced genomes, plasmids, and whole-genome shotgun assemblies available at NCBI for clinically important pathogens. CARD Prevalence 2.0.0 is based on sequence data acquired from NCBI on August 28, 2017, analyzed using RGI 4.0.0 (DIAMOND homolog detection) and CARD 2.0.0. Now includes results for protein overexpression models and rRNA mutations. All results organized by the revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Download files now include 35000+ genome annotations and all predicted sequence variants.

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d1-coverJia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, Doshi S, Courtot M, Lo R, Williams LE, Frye JG, Elsayegh T, Sardar D, Westman EL, Pawlowski AC, Johnson TA, Brinkman FS, Wright GD, & McArthur AG.

Nucleic Acids Res. 2017 Jan 4;45(D1):D566-D573.

The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.

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17-coverNi X, Davis JH, Jain N, Razi A, Benlekbir S, McArthur AG, Rubinstein JL, Britton RA, Williamson JR, Ortega J.

Nucleic Acids Res. 2016 Sep 30;44(17):8442-55.

YphC and YsxC are GTPases in Bacillus subtilis that facilitate the assembly of the 50S ribosomal subunit, however their roles in this process are still uncharacterized. To explore their function, we used strains in which the only copy of the yphC or ysxC genes were under the control of an inducible promoter. Under depletion conditions, they accumulated incomplete ribosomal subunits that we named 45SYphC and 44.5SYsxC particles. Quantitative mass spectrometry analysis and the 5-6 Å resolution cryo-EM maps of the 45SYphC and 44.5SYsxC particles revealed that the two GTPases participate in the maturation of the central protuberance, GTPase associated region and key RNA helices in the A, P and E functional sites of the 50S subunit. We observed that YphC and YsxC bind specifically to the two immature particles, suggesting that they represent either on-pathway intermediates or that their structure has not significantly diverged from that of the actual substrate. These results describe the nature of these immature particles, a widely used tool to study the assembly process of the ribosome. They also provide the first insights into the function of YphC and YsxC in 50S subunit assembly and are consistent with this process occurring through multiple parallel pathways, as it has been described for the 30S subunit.

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genome-canada-1A 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.

Full Coverage: Faculty of Health Sciences, Genome Canada, Newswire, Hamilton Spectator

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Andrew-judy627

Combatting Antibiotic Resistance Using Surveillance – click on the image to watch the 10 minute video. More details here.

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Click on the image to view the complete issue online

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Andrew-judy627Dr. 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.

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