Glycopeptide antibiotics are produced by Actinobacteria through biosynthetic gene clusters that include genes supporting their regulation, synthesis, export and resistance. The chemical and biosynthetic diversities of glycopeptides are the product of an intricate evolutionary history. Extracting this history from genome sequences is difficult as conservation of the individual components of these gene clusters is variable and each component can have a different trajectory. We show that glycopeptide biosynthesis and resistance in Actinobacteria maps to approximately 150-400 million years ago. Phylogenetic reconciliation reveals that the precursors of glycopeptide biosynthesis are far older than other components, implying that these clusters arose from a pre-existing pool of genes. We find that resistance appeared contemporaneously with biosynthetic genes, raising the possibility that the mechanism of action of glycopeptides was a driver of diversification in these gene clusters. Our results put antibiotic biosynthesis and resistance into an evolutionary context and can guide the future discovery of compounds possessing new mechanisms of action, which are especially needed as the usefulness of the antibiotics available at present is imperilled by human activity.
More details at McMaster’s Brighter World.
Dr. McArthur and PhD student Kara Tsang taught together at the 2019 MacData Institute Summer School, with Dr. McArthur reviewing biocuration and bioinformatics for genomic surviellence of antimicrobial resistance and Kara following up with a lecture on machine learning techniques to predict clinical antimicrobial resistance from raw genomic sequence.
Also congratulations to Kara for being awarded a 2019 Faculty of Health Sciences Graduate Programs Excellence Award!
Updated August 6, 2019: Congratulations to Kara for also winning an Ontario Graduate Scholarship!
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.
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.
Jia 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.
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.
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.
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.
Combatting Antibiotic Resistance Using Surveillance – click on the image to watch the 10 minute video. More details here.