The Comprehensive Antibiotic Resistance Database (CARD; http://arpcardmcmasterca) 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

CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database

The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD's Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

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CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

Klebsiella pneumoniae is now recognized as an urgent threat to human health because of the emergence of multidrug-resistant strains associated with hospital outbreaks and hypervirulent strains associated with severe community-acquired infections. K. pneumoniae is ubiquitous in the environment and can colonize and infect both plants and animals. However, little is known about the population structure of K. pneumoniae, so it is difficult to recognize or understand the emergence of clinically important clones within this highly genetically diverse species. Here we present a detailed genomic framework for K. pneumoniae based on whole-genome sequencing of more than 300 human and animal isolates spanning four continents. Our data provide genome-wide support for the splitting of K. pneumoniae into three distinct species, KpI (K. pneumoniae), KpII (K. quasipneumoniae), and KpIII (K. variicola). Further, for K. pneumoniae (KpI), the entity most frequently associated with human infection, we show the existence of >150 deeply branching lineages including numerous multidrug-resistant or hypervirulent clones. We show K. pneumoniae has a large accessory genome approaching 30,000 protein-coding genes, including a number of virulence functions that are significantly associated with invasive community-acquired disease in humans. In our dataset, antimicrobial resistance genes were common among human carriage isolates and hospital-acquired infections, which generally lacked the genes associated with invasive disease. The convergence of virulence and resistance genes potentially could lead to the emergence of untreatable invasive K. pneumoniae infections; our data provide the whole-genome framework against which to track the emergence of such threats.

https://www.pnas.org/content/pnas/112/27/E3574.full.pdf

Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health

Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data. Source code is available from http://katholt.github.io/srst2/.

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/262273/PMC4237778.pdf

SRST2: Rapid genomic surveillance for public health and hospital microbiology labs

Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.

/pdf/validating-the-amrfinder-tool-and-resistance-gene-database-4iev97zn99.pdf

Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates.

ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.

ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes

INSERM U895, C3M, 151 Route de Ginestiere, BP 2 3194, F-06204 NICE Cedex 3, France 13 * Corresponding author 14 Tel: (33). 04.91.32.43.75, Fax: (33). 04.91.38.77.72 15 E-mail: 1 16 Abstract words: 246 17 Text words: 3685 18 References: 30 19 20 AAC Accepts, published online ahead of print on 21 October 2013Antimicrob. Agents Chemother. doi:10.1128/AAC.01310-13Copyright © 2013, American Society for Microbiology. All Rights Reserved.

Babu Roshan Padmanabhan

Papers

ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes

RUNNING TITLE: Detection of antibiotic resistance genes in bacterial genomes 3