CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database
TL;DR: A new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes, able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants.
Abstract: 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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TL;DR: In this article, the authors present the current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic.
Abstract: Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks.
383 citations
TL;DR: The Metagenomic Gut Virus catalogue as discussed by the authors contains 189,680 genomes from 11,810 publicly available human stool metagenomes and identified 54,118 candidate viral species, 92% of which were not found in existing databases.
Abstract: Bacteriophages have important roles in the ecology of the human gut microbiome but are under-represented in reference databases. To address this problem, we assembled the Metagenomic Gut Virus catalogue that comprises 189,680 viral genomes from 11,810 publicly available human stool metagenomes. Over 75% of genomes represent double-stranded DNA phages that infect members of the Bacteroidia and Clostridia classes. Based on sequence clustering we identified 54,118 candidate viral species, 92% of which were not found in existing databases. The Metagenomic Gut Virus catalogue improves detection of viruses in stool metagenomes and accounts for nearly 40% of CRISPR spacers found in human gut Bacteria and Archaea. We also produced a catalogue of 459,375 viral protein clusters to explore the functional potential of the gut virome. This revealed tens of thousands of diversity-generating retroelements, which use error-prone reverse transcription to mutate target genes and may be involved in the molecular arms race between phages and their bacterial hosts.
159 citations
TL;DR: (meta)proteomics analysis of bacterial compartment of raw milk is applied to obtain a method that provides a measurement of circulating AMR involved proteins and gathers information about the whole bacterial composition.
Abstract: The environment, including animals and animal products, is colonized by bacterial species that are typical and specific of every different ecological niche. Natural and human-related ecological pressure promotes the selection and expression of genes related to antimicrobial resistance (AMR). These genes might be present in a bacterial consortium but might not necessarily be expressed. Their expression could be induced by the presence of antimicrobial compounds that could originate from a given ecological niche or from human activity. In this work, we applied (meta)proteomics analysis of bacterial compartment of raw milk in order to obtain a method that provides a measurement of circulating AMR involved proteins and gathers information about the whole bacterial composition. Results from milk analysis revealed the presence of 29 proteins/proteoforms linked to AMR. The detection of mainly β-lactamases suggests the possibility of using the milk microbiome as a bioindicator for the investigation of AMR. Moreover, it was possible to achieve a culture-free qualitative and functional analysis of raw milk bacterial consortia.
121 citations
Cites methods from "CARD 2020: antibiotic resistome sur..."
...Among other proteins with AMR potential identified using the CARD15 database there is an isoform of the Aminoglycoside N(6’)-acetyltransferase of Enterococcus hirae....
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...In order to identify the whole bacterial proteome, the obtained MS datasets were analyzed using different databases: UniProt KB/Swiss-Prot restricted to all reviewed Bacteria protein sequences (UniProt KB) and the Comprehensive Antibiotic Resistance Database (CARD) [14]....
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...Figure 3 shows the Venn diagram of the proteins identified in the two extractions using the CARD 15 database....
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...The same raw MS dataset was then searched against the CARD 15 database....
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...The qualitative identification of proteins was obtained by searching two different databases: (i) bacteria (UniProt KB/Swiss-Prot Protein Knowledgebase restricted to all Bacteria taxonomy) and (ii) The Comprehensive AMR Database (CARD, https://card.mcmaster.ca/) as FASTA files [13,14]....
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TL;DR: In this paper , the authors present a platform consisting of three modules, which are preconfigured bioinformatic pipelines, cloud toolsets, and online omics' courses, which combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics.
Abstract: The platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.
121 citations
University of Dhaka1, Loyola University Chicago2, BGC Trust University Bangladesh3, Indian Veterinary Research Institute4, Mawlana Bhashani Science and Technology University5, University of Szeged6, Sarhad University of Science & IT, Ring Road (Hayatabad Link) Peshawar7, State University of Bangladesh8, Kathmandu9
TL;DR: Antibiotics have been used to cure bacterial infections for more than 70 years, and these low-molecular-weight bioactive agents have also been used for a variety of other medicinal applications.
99 citations
References
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TL;DR: The Repository of Antibiotic resistance Cassettes (RAC) website is developed to provide an archive of gene cassettes that includes alternative gene names from multiple nomenclature systems and allows the community to contribute new cassettes.
Abstract: Antibiotic resistance in bacteria is often due to acquisition of resistance genes associated with different mobile genetic elements. In Gram-negative bacteria, many resistance genes are found as part of small mobile genetic elements called gene cassettes, generally found integrated into larger elements called integrons. Integrons carrying antibiotic resistance gene cassettes are often associated with mobile elements and here are designated 'mobile resistance integrons' (MRIs). More than one cassette can be inserted in the same integron to create arrays that contribute to the spread of multi-resistance. In many sequences in databases such as GenBank, only the genes within cassettes, rather than whole cassettes, are annotated and the same gene/cassette may be given different names in different entries, hampering analysis. We have developed the Repository of Antibiotic resistance Cassettes (RAC) website to provide an archive of gene cassettes that includes alternative gene names from multiple nomenclature systems and allows the community to contribute new cassettes. RAC also offers an additional function that allows users to submit sequences containing cassettes or arrays for annotation using the automatic annotation system Attacca. Attacca recognizes features (gene cassettes, integron regions) and identifies cassette arrays as patterns of features and can also distinguish minor cassette variants that may encode different resistance phenotypes (aacA4 cassettes and bla cassettes-encoding β-lactamases). Gaps in annotations are manually reviewed and those found to correspond to novel cassettes are assigned unique names. While there are other websites dedicated to integrons or antibiotic resistance genes, none includes a complete list of antibiotic resistance gene cassettes in MRI or offers consistent annotation and appropriate naming of all of these cassettes in submitted sequences. RAC thus provides a unique resource for researchers, which should reduce confusion and improve the quality of annotations of gene cassettes in integrons associated with antibiotic resistance. DATABASE URL: http://www2.chi.unsw.edu.au/rac.
58 citations
"CARD 2020: antibiotic resistome sur..." refers background in this paper
...Many of these are highly focused on, for example, metagenomics of environmental AMR (14), profiling for AMR conferring mutations in Mycobacterium tuberculosis (15) or collation of AMR-associated transposable elements (16)....
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TL;DR: FARME is the first database to focus on functional metagenomic AR gene elements and provides a resource to better understand AR in the 99% of bacteria which cannot be cultured and the relationship between environmental AR sequences and antibiotic resistant genes derived from cultured isolates.
Abstract: Antibiotic resistance (AR) is a major global public health threat but few resources exist that catalog AR genes outside of a clinical context. Current AR sequence databases are assembled almost exclusively from genomic sequences derived from clinical bacterial isolates and thus do not include many microbial sequences derived from environmental samples that confer resistance in functional metagenomic studies. These environmental metagenomic sequences often show little or no similarity to AR sequences from clinical isolates using standard classification criteria. In addition, existing AR databases provide no information about flanking sequences containing regulatory or mobile genetic elements. To help address this issue, we created an annotated database of DNA and protein sequences derived exclusively from environmental metagenomic sequences showing AR in laboratory experiments. Our Functional Antibiotic Resistant Metagenomic Element (FARME) database is a compilation of publically available DNA sequences and predicted protein sequences conferring AR as well as regulatory elements, mobile genetic elements and predicted proteins flanking antibiotic resistant genes. FARME is the first database to focus on functional metagenomic AR gene elements and provides a resource to better understand AR in the 99% of bacteria which cannot be cultured and the relationship between environmental AR sequences and antibiotic resistant genes derived from cultured isolates.Database URL: http://staff.washington.edu/jwallace/farme.
45 citations
"CARD 2020: antibiotic resistome sur..." refers background in this paper
...Many of these are highly focused on, for example, metagenomics of environmental AMR (14), profiling for AMR conferring mutations in Mycobacterium tuberculosis (15) or collation of AMR-associated transposable elements (16)....
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TL;DR: This review, based on different crystallographic structures solved from archetypal bacteria, will first focus on the molecular mechanism of the regulator families involved in the RND efflux pump expression in P. aeruginosa, which are TetR, LysR, MarR, AraC, and the two-components system (TCS).
Abstract: Bacterial antibiotic resistance is a worldwide health problem that deserves important research attention in order to develop new therapeutic strategies. Recently, the World Health Organization (WHO) classified Pseudomonas aeruginosa as one of the priority bacteria for which new antibiotics are urgently needed. In this opportunistic pathogen, antibiotics efflux is one of the most prevalent mechanisms where the drug is efficiently expulsed through the cell-wall. This resistance mechanism is highly correlated to the expression level of efflux pumps of the resistance-nodulation-cell division (RND) family, which is finely tuned by gene regulators. Thus, it is worthwhile considering the efflux pump regulators of P. aeruginosa as promising therapeutical targets alternative. Several families of regulators have been identified, including activators and repressors that control the genetic expression of the pumps in response to an extracellular signal, such as the presence of the antibiotic or other environmental modifications. In this review, based on different crystallographic structures solved from archetypal bacteria, we will first focus on the molecular mechanism of the regulator families involved in the RND efflux pump expression in P. aeruginosa, which are TetR, LysR, MarR, AraC, and the two-components system (TCS). Finally, the regulators of known structure from P. aeruginosa will be presented.
40 citations
06 Aug 2019
TL;DR: The creation of Meta-MARC is presented, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models and has higher sensitivity relative to competing methods, which allows for detection of sequences that are divergent from known antimicrobial resistance genes.
Abstract: The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases. Steven Lakin et al. present Meta-MARC, a computational method for identifying antimicrobial resistance sequences using DNA-based Hidden Markov Models. Because of its increased sensitivity, Meta-MARC is able to detect novel antimicrobial resistance sequences that are divergent from all known sequences.
27 citations