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.
Citations
More filters
TL;DR: Analysis of antibiotic gene clusters in P. polymyxa revealed the presence of these genes in both core and accessory genomes, indicating that the difference in living environment led to loss of ability to synthesize antibiotics in some strains.
Abstract: Rapid development of gene sequencing technologies has led to an exponential increase in microbial sequencing data. Genome research of a single organism does not capture the changes in the characteristics of genetic information within a species. Pan-genome analysis gives us a broader perspective to study the complete genetic information of a species. Paenibacillus polymyxa is a Gram-positive bacterium and an important plant growth-promoting rhizobacterium with the ability to produce multiple antibiotics, such as fusaricidin, lantibiotic, paenilan, and polymyxin. Our study explores the pan-genome of 14 representative P. polymyxa strains isolated from around the world. Heap's law model and curve fitting confirmed an open pan-genome of P. polymyxa. The phylogenetic and collinearity analyses reflected that the evolutionary classification of P. polymyxa strains are not associated with geographical area and ecological niches. Few genes related to phytohormone synthesis and phosphate solubilization were conserved; however, the nif cluster gene associated with nitrogen fixation exists only in some strains. This finding is indicative of nitrogen fixing ability is not stable in P. polymyxa. Analysis of antibiotic gene clusters in P. polymyxa revealed the presence of these genes in both core and accessory genomes. This observation indicates that the difference in living environment led to loss of ability to synthesize antibiotics in some strains. The current pan-genomic analysis of P. polymyxa will help us understand the mechanisms of biological control and plant growth promotion. It will also promote the use of P. polymyxa in agriculture.
17 citations
TL;DR: How chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities are discussed.
Abstract: Horizontal gene transfer is an important mechanism of microbial evolution and is often driven by the movement of mobile genetic elements between cells. Due to the fact that microbes live within communities, various mechanisms of horizontal gene transfer and types of mobile elements can co-occur. However, the ways in which horizontal gene transfer impacts and is impacted by communities containing diverse mobile elements has been challenging to address. Thus, the field would benefit from incorporating community-level information and novel approaches alongside existing methods. Emerging technologies for tracking mobile elements and assigning them to host organisms provide promise for understanding the web of potential DNA transfers in diverse microbial communities more comprehensively. Compared to existing experimental approaches, chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts. We also briefly discuss how fermented food microbiomes, given their experimental tractability and moderate species complexity, make ideal models to which to apply the techniques discussed herein and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities.
17 citations
TL;DR: Third-generation cephalosporin-resistant Gram-negatives and vancomycin-resistant Enterococci and VRE are common causes of multi-drug resistant healthcare-associated infections, but there remains a key knowledge gap about colonisation and infection dynamics in high-risk settings such as the intensive care unit (ICU), thus hampering infection prevention efforts.
Abstract: Third-generation cephalosporin-resistant Gram-negatives (3GCR-GN) and vancomycin-resistant enterococci (VRE) are common causes of multi-drug resistant healthcare-associated infections, for which gut colonisation is considered a prerequisite. However, there remains a key knowledge gap about colonisation and infection dynamics in high-risk settings such as the intensive care unit (ICU), thus hampering infection prevention efforts. We performed a three-month prospective genomic survey of infecting and gut-colonising 3GCR-GN and VRE among patients admitted to an Australian ICU. Bacteria were isolated from rectal swabs (n = 287 and n = 103 patients ≤2 and > 2 days from admission, respectively) and diagnostic clinical specimens between Dec 2013 and March 2014. Isolates were subjected to Illumina whole-genome sequencing (n = 127 3GCR-GN, n = 41 VRE). Multi-locus sequence types (STs) and antimicrobial resistance determinants were identified from de novo assemblies. Twenty-three isolates were selected for sequencing on the Oxford Nanopore MinION device to generate completed reference genomes (one for each ST isolated from ≥2 patients). Single nucleotide variants (SNVs) were identified by read mapping and variant calling against these references. Among 287 patients screened on admission, 17.4 and 8.4% were colonised by 3GCR-GN and VRE, respectively. Escherichia coli was the most common species (n = 36 episodes, 58.1%) and the most common cause of 3GCR-GN infection. Only two VRE infections were identified. The rate of infection among patients colonised with E. coli was low, but higher than those who were not colonised on admission (n = 2/33, 6% vs n = 4/254, 2%, respectively, p = 0.3). While few patients were colonised with 3GCR- Klebsiella pneumoniae or Pseudomonas aeruginosa on admission (n = 4), all such patients developed infections with the colonising strain. Genomic analyses revealed 10 putative nosocomial transmission clusters (≤20 SNVs for 3GCR-GN, ≤3 SNVs for VRE): four VRE, six 3GCR-GN, with epidemiologically linked clusters accounting for 21 and 6% of episodes, respectively (OR 4.3, p = 0.02). 3GCR-E. coli and VRE were the most common gut colonisers. E. coli was the most common cause of 3GCR-GN infection, but other 3GCR-GN species showed greater risk for infection in colonised patients. Larger studies are warranted to elucidate the relative risks of different colonisers and guide the use of screening in ICU infection control.
17 citations
Cites background from "CARD 2020: antibiotic resistome sur..."
...A number of hospitals have implemented rectal screening programs to identify patients colonised with VRE and/or carbapenemaseproducing Enterobacteriaceae in high-risk wards such as intensive care units (ICUs), oncology and hematology wards [7,8,13,14]....
[...]
...Consequently, it is not uncommon to find near-identical isolates in patients from different wards, hospitals or cities without epidemiological links [7,8,13]....
[...]
TL;DR: Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.
Abstract: A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases where the primary AMR mechanism results from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes for use in these models, we show that F1 scores and error rates are stable and have little variance between replicates. Potential biases from strain-specific SNPs, phylogenetic sampling, and imbalances in the phylogenetic distribution of susceptible and resistant strains do not appear to have an impact on this result. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes. Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.
17 citations
TL;DR: This review compares the most well-known and widely used antibiotic resistance gene databases based on their structure and content to help select the most appropriate database for a research question and for the development of new tools and resources of resistance gene annotation.
Abstract: As the prevalence of antimicrobial resistance genes is increasing in microbes, we are facing the return of the pre-antibiotic era. Consecutively, the number of studies concerning antibiotic resistance and its spread in the environment is rapidly growing. Next generation sequencing technologies are widespread used in many areas of biological research and antibiotic resistance is no exception. For the rapid annotation of whole genome sequencing and metagenomic results considering antibiotic resistance, several tools and data resources were developed. These databases, however, can differ fundamentally in the number and type of genes and resistance determinants they comprise. Furthermore, the annotation structure and metadata stored in these resources can also contribute to their differences. Several previous reviews were published on the tools and databases of resistance gene annotation; however, to our knowledge, no previous review focused solely and in depth on the differences in the databases. In this review, we compare the most well-known and widely used antibiotic resistance gene databases based on their structure and content. We believe that this knowledge is fundamental for selecting the most appropriate database for a research question and for the development of new tools and resources of resistance gene annotation.
16 citations
References
More filters
TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
88,255 citations
"CARD 2020: antibiotic resistome sur..." refers background in this paper
...The latter is described by CARD’s Model Ontology (MO, Supplementary Figure S1), which includes reference nucleotide and protein sequences, as well as additional search parameters including mutations conferring AMR (if applicable) and curated BLAST(P/N) (34,35) bit score cut-offs....
[...]
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.
Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.
Availability: http://maq.sourceforge.net
Contact: [email protected]
43,862 citations
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
37,898 citations
"CARD 2020: antibiotic resistome sur..." refers methods in this paper
...Metagenomics analysis (i.e. RGI bwt) uses Bowtie2 (40) or BWA (41) mapping of sequencing reads to CARD’s PHM reference sequences only, while annotation of genomes or assembly contigs predicts resistome using four of CARD’s AMR detection models: PHM, PVM, RVM and POM (note: RGI currently only scans for nonsynonymous substitutions; not frameshifts, deletions or insertions)....
[...]
...RGI bwt) uses Bowtie2 (40) or BWA (41) mapping of sequencing reads to CARD’s PHM reference sequences only, while annotation of genomes or assembly contigs predicts resistome using four of CARD’s AMR detection models: PHM, PVM, RVM and POM (note: RGI currently only scans for nonsynonymous substitutions; not frameshifts, deletions or insertions)....
[...]
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Abstract: The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
34,239 citations
"CARD 2020: antibiotic resistome sur..." refers methods in this paper
...In 2017, we described the CARD*Shark text-mining algorithm (26) for computer-assisted literature triage, which we have expanded based on the new ARO Drug Class classification tags....
[...]
TL;DR: The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences.
Abstract: Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
13,223 citations
"CARD 2020: antibiotic resistome sur..." refers background or methods in this paper
...The website also includes a built-in BLAST instance for comparing sequences to CARD reference sequences and a web instance of RGI for resistome prediction with data visualization tools (https:// card.mcmaster.ca/analyze)....
[...]
...The RVM is functionally similar to the PVM, except it works for rRNA mutations and therefore uses a nucleotide reference sequence and a BLASTN bit score cut-off....
[...]
...Briefly, RGI algorithmically predicts AMR genes and mutations from submitted genomes using a combination of open reading frame prediction with Prodigal (38), sequence alignment with BLAST (35) or DIAMOND (39), and curated resistance mutations included with the AMR detection model....
[...]
...In the same time period, the CARD website hosted ∼45 000 BLAST analyses, ∼220 000 RGI analyses, ∼64 000 data file downloads, and ∼10,000 RGI software downloads....
[...]
...We had determined that the asymptotic nature of the BLAST expectation value (E) gave it very low discriminatory power between different -lactamase gene families (nearly 13 of CARD’s content), but that the linear nature of the BLAST bit score (S′) allowed this level of discrimination....
[...]