New Delhi Metallo-Beta-Lactamase (NDM) plasmid predictions
The complete detection and genome sequencing of resistance plasmids is required to understand the co-occurrence of resistance genes on plasmids and their transmission, which is necessary to mitigate the spread of drug resistant bacterial infections. One group of multi-drug resistance plasmids of interest are the New Delhi Metallo-beta-lactamase (NDM) associ-ated plasmids. Bacteria pathogens with NDM genes are resistant to a broad range of beta-lactam antibiotics, including carbapenems, a mainstay for treating bacterial infections. We sequenced clinical isolates collected from patients that failed to respond to antibiotic treatments in the Hamilton-Niagara community using Next Generation Sequencing (NGS) technology, which is the standard technique for sequencing bacterial genomes. I analyzed their assembled genomes via the development of a new resistance plasmid prediction bioinformatics pipeline: RGI:Mobilome. RGI:Mobilome predicted NDM bearing plasmids in 15 isolates, along with other resistance genes on plasmids. However, the NDM plasmid predictions of all 15 isolates were fragmented due to incomplete genome assemblies, caused by repetitive sequences within bacterial genomes and use of short-read NGS technology. However, plasmid predictions greatly improved when we leveraged the high nucleotide accuracy of NGS reads and the structural resolving power of long reads generated with the Oxford Nanopore Technology (ONT) to produce ‘hybrid’ genome assemblies. From the 15 putative hybrid assembly NDM plasmids, one plasmid was a complete match to a known pKP-NDM1 plasmid, seven plasmids were “variants” of known and well characterized plasmids, and the remaining seven plasmids were “distant homologs” of known and well characterized plasmids, suggesting previously undescribed NDM-associated plasmids in our community. This thesis project reveals the diversity of NDM plasmid types within the Hamilton-Niagara community, which is valuable to epidemiologists and public health practitioners to devise actionable plans required to mitigate the spread of multi-drug resistance NDM plasmids and for clinicians to treat infections caused by NDM positive strains.
Jalees A. Nasir, Robert A. Kozak, Patryk Aftanas, Amogelang R. Raphenya, Kendrick M. Smith, Finlay Maguire, Hassaan Maan, Muhannad Alruwaili, Arinjay Banerjee, Hamza Mbareche, Brian P. Alcock, Natalie C. Knox, Karen Mossman, Bo Wang, Julian A. Hiscox, Andrew G. McArthur, & Samira Mubareka
Genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is increasingly important to monitor the transmission and adaptive evolution of the virus. The accessibility of high-throughput methods and polymerase chain reaction (PCR) has facilitated a growing ecosystem of protocols. Two differing protocols are tiling multiplex PCR and bait capture enrichment. Each method has advantages and disadvantages but a direct comparison with different viral RNA concentrations has not been performed to assess the performance of these approaches. Here we compare Liverpool amplification, ARTIC amplification, and bait capture using clinical diagnostics samples. All libraries were sequenced using an Illumina MiniSeq with data analyzed using a standardized bioinformatics workflow (SARS-CoV-2 Illumina GeNome Assembly Line; SIGNAL). One sample showed poor SARS-CoV-2 genome coverage and consensus, reflective of low viral RNA concentration. In contrast, the second sample had a higher viral RNA concentration, which yielded good genome coverage and consensus. ARTIC amplification showed the highest depth of coverage results for both samples, suggesting this protocol is effective for low concentrations. Liverpool amplification provided a more even read coverage of the SARS-CoV-2 genome, but at a lower depth of coverage. Bait capture enrichment of SARS-CoV-2 cDNA provided results on par with amplification. While only two clinical samples were examined in this comparative analysis, both the Liverpool and ARTIC amplification methods showed differing efficacy for high and low concentration samples. In addition, amplification-free bait capture enriched sequencing of cDNA is a viable method for generating a SARS-CoV-2 genome sequence and for identification of amplification artifacts.
SARS-CoV-2 is a novel betacoronavirus and the aetiological agent of the current COVID-19 outbreak that originated in Hubei Province, China. While polymerase chain reaction is the front-line tool for SARS-CoV-2 surveillance, application of amplification-free and culture-free methods for isolation of SARS-CoV-2 RNA, partnered with next-generation sequencing, would provide a useful tool for both surveillance and research of SARS-CoV-2. We here release into the public domain a set of bait capture hybridization probe sequences for enrichment of SARS-CoV-2 RNA from complex biological samples. These probe sequences have been designed using rigorous bioinformatics methods to provide sensitivity, accuracy, and minimal off-target hybridization. Probe design was based on existing, validated approaches for detecting antimicrobial resistance genes in complex samples and it is our hope that this SARS-CoV-2 bait capture platform, once validated by those with samples in hand, will be of aid in combating the current outbreak.
Data, Software, and Sequences: https://github.com/jaleezyy/covid-19-baits
Full Story at Brighter World: McMaster develops tool for coronavirus battle
During McMaster Spring Mid-Term Recess (February 18-24), the McArthur lab is pleased to present a series of lectures, demonstrations, and training sessions for the Comprehensive Antibiotic Resistance Database (card.mcmaster.ca) and its associated Resistance Gene Identifier (RGI) software, sponsored by the Michael G. DeGroote Institute for Infectious Disease Research (IIDR).
Questions? Email email@example.com
Workshop & Lecture material will be available here: https://github.com/arpcard/state-of-the-card-2019
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 McArthur lab and the Comprehensive Antibiotic Resistance Database are proud to join the Canadian Anti-Infective Innovation Network, International Genomic Epidemiology Application Ontology Consortium, and Integrated Rapid Infectious Disease Analysis Project!
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.
Building upon her successful Biochem 3A03 project, Tammy Lau is staying in the lab for 2017-2018 as part of her Biochem 4T15 Research Thesis. Tammy’s research will be focussed on developing new classification and visualization tools for our Resistance Gene Identifier (RGI), plus extending the RGI towards k-mer approaches for predicting pathogen-of-origin for metagenomics antimicrobial resistance gene sequences.
Suman Virdee – Developing a Galaxy based Pipeline for RNA-Seq Analysis in Stem Cell Biology
Kirill Pankov – The Cytochrome P450 (CYP) Superfamily in the Cnidarian Phylum
Jonsson Liu – Clinical virulence detection and Clostridium difficile clonality
Annie Cheng – Predicting Plasmid-Mediated Antimicrobial Resistance from Whole Genome Sequencing
Godwin Chan – Using the Galaxy Platform to Increase Accessibility for Structure Determination via Cryo-Electron Microscopy