Fsis Form 6200 20 Establishment Sorting Record
Fsis Form 6200 20 Establishment Sorting Record – New results using the NCBI AMRFinder tool to determine antimicrobial resistance genotype-phenotype associations within a collection of NARMS isolates
Michael Feldgarden, Vyacheslav Brover, Daniel H. Haft, Arjun B. Prasad, Douglas J. Slotta, Igor Tolstoy, Gregory H. Tyson, Shaohua Zhao, Chih-Hao Hsu, Patrick F. McDermott, Daniel A. Tadesse, Mustafa Simmons, Glenn Tillman, Jamie Wasilenko, Jason P. Folster, William Klimke
Fsis Form 6200 20 Establishment Sorting Record
Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify acquired AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) created a high-quality, curated AMR gene reference database consisting of updated protein and gene nomenclature, a set of hidden Markov models (HMMs ), and a composite protein family hierarchy. Currently, the bacterial antimicrobial resistance gene reference database contains 4,579 antimicrobial resistance gene proteins and more than 560 HMMs.
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Here we describe AMRFinder, a tool that uses this reference dataset to identify AMR genes. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder against resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425
To evaluate the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene database. Most gene calls were identical, but there were 1,229 gene symbol differences between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci found by Resfinder, while Resfinder missed 1,147 loci found by AMRFinder. Two drug classes missing from the 2017 version of ResFinder contributed to 81% of the missing sites. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
Importance Antimicrobial resistance is a major public health problem. Traditionally, antimicrobial resistance has been identified using phenotypic tests. With the advent of genome sequencing, we can now identify resistance genes and deduce whether an isolate may be resistant to antibiotics. We describe a database of 4,579 acquired antimicrobial resistance genes, the largest publicly available, and a software tool for gene identification in bacterial genomes, AMRFinder. Unlike other tools, AMRFinder uses a gene hierarchy to prevent overprediction of correct gene calls, allowing for more accurate assessment. To evaluate these resources, we determined the resistance gene content of more than 6,200 bacterial isolates from the National Antimicrobial Resistance Surveillance System that were analyzed using traditional methods and also had their genomes sequenced. We also compared our gene ratings to those of a widely used tool. We found that AMRFinder has high overall concordance between genotypes and phenotypes.
Antimicrobial resistance (AMR) is a major public health problem, with an estimated 23,000 deaths per year in the US attributable to antimicrobial-resistant infections ( https://www.cdc.gov/drugresistance/threat-report-2013 /index.html ). The continued evolution of multidrug resistance ensures that AMR will continue to be a health challenge for years to come. As described in the National Strategy for Combating Antibiotic-Resistant Bacteria report (https://www.cdc.gov/drugresistance/pdf/national_action_plan_for_combating_antibotic-resistant_bacteria.pdf), there is a critical need to understand how AMR with bacterial genotype, and both to improve AMR mechanism discovery and to enable AMR diagnostics. A key method to establish this link is genome sequencing, which can also be used for surveillance purposes.
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Traditionally, AMR has been identified using phenotypic tests. The gold standard for measuring antimicrobial susceptibility is based on standard dilution or diffusion
Antimicrobial susceptibility testing (AST) methods where extensive research and trials have been conducted to correlate AST measurements with clinical outcomes (1) Molecular methods are increasingly used in resistance surveillance and in some cases also to guide clinical therapy. These range from PCR detection of known resistance elements (2) to mass spectrometry-based methods (3-7). Whole-genome shotgun sequencing (WGS) has been incorporated into clinical and public health settings, although the use of WGS has focused primarily on outbreak detection and monitoring (8, 9). Along with epidemiological uses, there is great potential for using WGS to aid and guide AMR detection (10-15). Accurate assessment of AMR gene content enables the discovery of new resistance variants and can serve as a basis for predicting resistance phenotypes without the need for time-consuming phenotypic testing (11, 16, 17).
An in-silico approach to assess AMR content requires comprehensive and accurate AMR gene databases, as well as tools that can reliably identify AMR genes. There are many databases and tools that use a variety of approaches and data sources, as described in a recent review (18). While some tools use exclusively BLAST-based approaches (19), others include Hidden Markov Model (HMM) approaches (20). BLAST-based approaches are able to identify specific alleles and closely related genes. However, BLAST-based methods use arbitrary cutoffs that may falsely call AMR genes or even falsely attribute resistance to non-AMR genes (eg, misidentifying metallo-beta hydrolases as metallo-beta-lactamases (21)). HMM approaches facilitate a hierarchical classification of AMR proteins, from alleles to gene families, but assembly and validation of HMM libraries is required. The tools also differ depending on whether they analyze nucleotide or protein sequence. Additionally, some tools are only available through a web interface, while others can run on local servers giving users more flexibility. Researchers trying to use the currently available AMR databases must choose between these different database sources. Some contain collections of resistance gene alignments for use in HMMs (20). Others consist of collections of nucleotide or protein sequences from either individual resistance genes or resistance-associated mobile elements (22, 23). Some databases are being actively compiled, such as the CARD database (23, 24), ResFinder (22) and the Lahey Clinic database (https://www.lahey.org/Studies/), the latter now maintained by the NCBI, as part of the NCBI Bacterial Antimicrobial Resistance No Database Reference), while others are not actively updated. Different classes of genes are edited by separate groups, and even one class of genes can be edited by multiple groups (eg beta-lactamases). In addition, some data sources include allelic variation in genes that may confer or contribute to resistance, while others focus exclusively on mechanisms of acquired resistance. The evaluation and comparison of these resources and tools is also challenging, as there are few collections of high-quality strains that have been comprehensively genotyped and phenotyped and are also publicly available.
Here we describe the development of a comprehensive AMR gene database, the bacterial antimicrobial resistance gene reference base, and the development of AMRFinder, an AMR gene identification tool, together with publicly available datasets for testing AMR gene detection methods. To identify AMR genes from sequence data, we generated more than 560 AMR HMMs (21) and assembled more than 4,579 AMR protein sequences, placing both in a hierarchical framework of gene families, symbols, and names in collaboration with various groups , including CARD (21) . We then developed AMRFinder to exploit both the content and structure of this database to accurately locate and name AMR gene sequences. To validate this system, we used a collection of isolates from the NARMS program that underwent extensive sensitivity testing and whole genome assembly, and also compared the performance of AMRFinder with a version of ResFinder 2.0 released in 2017.
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The Bacterial Antimicrobial Resistance Reference Gene Database contains a hierarchy of AMR protein families and is stored in NCBI’s RefSeq database (21). Each protein, and each protein family, has an edited name and gene symbol where appropriate. Gene symbols can point to more than one protein sequence, while alleles to a single amino acid sequence. For many families, we built protein HMMs that identify these protein families. When necessary, the protein sequence was manually verified to be full-length and to have the appropriate start site. Proteins are classified into protein family hierarchies based on protein homology and function.
Our collection of AMR proteins comes from several sources, including the collection of beta-lactamase alleles and quinolone-resistant protein alleles of the Qnr family compiled by the Lahey Clinic group (http://www.lahey.org/studies/ ( 25) , ResFinder (22) and the Comprehensive Antimicrobial Resistance Database [MAP; (24)] At the request of the Lahey Clinic team of Dr. Karen Bush, George Jacoby and Timothy Palzkill (https://www.lahey.org) / Studies /), NCBI assumed responsibility for the assignment and assembly of beta-lactamase alleles (https://www.ncbi.nlm.nih.gov/pathogens/submit-beta-lactamase/). The assignment process uses several beta -lactamase subfamily HMMs that are also used by AMRFinder. Families covered include the 27 previously covered Lahey families, the ADC and PDC families, and the newly assigned CMH, CRH, and FRI families. By Janua As of 2016, NCBI has assigned 676 new beta-lactamase alleles. These newly assigned as well as previously edited alleles are incorporated into our AMR gene database. We have collected collections of resistance genes for various classes of ribosome-targeted antibiotics from Dr. Marilyn Roberts [(26) and personal communication]. We obtained collections of AMR proteins encoded in integron regions by both RAC ( 27 ) and INTEGRALL ( 28 ). Additional sources included collections maintained by collaborative groups such as the FDA Center for Veterinary Medicine, the University of Oxford (Dr. Derrick Crook) and the
Pasteur Institute Sequence Typing Database (http://bigsdb.pasteur.fr/klebsiella/klebsiella.html). These sources have been supplemented by continuous review of review articles and new reports of resistance proteins.
The 4,528 resistance proteins too
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