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

doi:10.1128/AAC.00483-19

. 2019 Oct 22;63(11):e00483-19.

doi: 10.1128/AAC.00483-19. Print 2019 Nov.

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

Michael Feldgarden ¹, Vyacheslav Brover ², Daniel H Haft ², Arjun B Prasad ², Douglas J Slotta ², Igor Tolstoy ², Gregory H Tyson ³, Shaohua Zhao ³, Chih-Hao Hsu ³, Patrick F McDermott ³, Daniel A Tadesse ³, Cesar Morales ⁴, Mustafa Simmons ⁴, Glenn Tillman ⁴, Jamie Wasilenko ⁴, Jason P Folster ⁵, William Klimke ²

Affiliations

¹ National Center for Biological Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA michael.feldgarden@nih.gov.
² National Center for Biological Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
³ Food and Drug Administration, Center for Veterinary Medicine, Office of Research, Laurel, Maryland, USA.
⁴ USDA Food Safety and Inspection Service, Office of Public Health Science, Eastern Laboratory, Athens, Georgia, USA.
⁵ Enteric Diseases Laboratory Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

PMID: 31427293
PMCID: PMC6811410
DOI: 10.1128/AAC.00483-19

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

Michael Feldgarden et al. Antimicrob Agents Chemother. 2019.

. 2019 Oct 22;63(11):e00483-19.

doi: 10.1128/AAC.00483-19. Print 2019 Nov.

Authors

Michael Feldgarden ¹, Vyacheslav Brover ², Daniel H Haft ², Arjun B Prasad ², Douglas J Slotta ², Igor Tolstoy ², Gregory H Tyson ³, Shaohua Zhao ³, Chih-Hao Hsu ³, Patrick F McDermott ³, Daniel A Tadesse ³, Cesar Morales ⁴, Mustafa Simmons ⁴, Glenn Tillman ⁴, Jamie Wasilenko ⁴, Jason P Folster ⁵, William Klimke ²

Affiliations

¹ National Center for Biological Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA michael.feldgarden@nih.gov.
² National Center for Biological Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
³ Food and Drug Administration, Center for Veterinary Medicine, Office of Research, Laurel, Maryland, USA.
⁴ USDA Food Safety and Inspection Service, Office of Public Health Science, Eastern Laboratory, Athens, Georgia, USA.
⁵ Enteric Diseases Laboratory Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

PMID: 31427293
PMCID: PMC6811410
DOI: 10.1128/AAC.00483-19

Erratum in

Erratum for Feldgarden et al., "Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates".
Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, Tyson GH, Zhao S, Hsu CH, McDermott PF, Tadesse DA, Morales C, Simmons M, Tillman G, Wasilenko J, Folster JP, Klimke W. Feldgarden M, et al. Antimicrob Agents Chemother. 2020 Mar 24;64(4):e00361-20. doi: 10.1128/AAC.00361-20. Print 2020 Mar 24. Antimicrob Agents Chemother. 2020. PMID: 32209564 Free PMC article. No abstract available.

Abstract

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.

Keywords: Campylobacter; Salmonella; analytical software; antimicrobial resistance; computational biology; database; foodborne pathogens; genomics; surveillance.

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Figures

FIG 1

FIG 1

Data processing and analysis flow. Processing steps and isolate inclusion and exclusion criteria are indicated by arrows, with the number of isolates retained in each phase indicated in the colored boxes. Thirty-eight isolates were excluded if their AST phenotypes in three or more drug classes differed from predictions based on acquired AMR genes.

FIG 2

FIG 2

Qnr loci affect ciprofloxacin (a) and nalidixic acid (b) MICs in S. enterica. Columns on the x axis correspond to observed MIC values; brackets below indicate the susceptible, intermediate, or resistant (SIR) values for those MICs. On the y axis, colored bars indicate the percentage of isolates sharing the same genotype with a given MIC value. Numbers above each column indicate the number of isolates observed with that MIC and genotypes. In the side legend, the number in parentheses is the number of isolates with the corresponding genotype. "No genes" indicates those isolates lacking any predicted fluoroquinolone resistance genes. oqxAB indicates the presence of these fluoroquinolone resistance genes in an isolate. "qnr" indicates the presence of one of the following Qnr family genes: QnrB2, QnrB19, QnrB77, QnrS1, QnrS2, or an unassigned QnrB family allele. "oqxAB, qnr" indicates an OqxAB, QnrB19 genotype. Point mutations for ciprofloxacin (a) are indicated by the gene in which they occurred, followed by the site and changed residues.

FIG 3

FIG 3

Beta-lactamases confer unexpected decreased susceptibility to amoxicillin-clavulanic acid in S. enterica. x and y axes in Fig. 3. Allelic variants within a beta-lactamase family are grouped together under the family name; an isolate can have multiple alleles belonging to the same family. bla_PSE family beta-lactamases are either CARB-2 or unassigned CARB alleles. bla_CMY family beta-lactamases were either novel bla_CMY alleles or the CMY-2 allele. bla_HER indicates either the HER-3 allele or a novel HER-family allele. bla_TEM indicates either a novel TEM allele or TEM-1. "No genes" indicates those isolates lacking beta-lactamases.

FIG 4

FIG 4

Gentamicin (a) and kanamycin (b) resistance in S. enterica. Format as described for Fig. 2 except aminoglycoside-modifying genes are grouped together by family. "No genes" indicates isolates lacking any predicted gentamicin and kanamycin resistance genes, respectively.

FIG 5

FIG 5

Schematic representation of the AMRFinder algorithm for (a) searching protein sequences and (b) searching nucleotide sequences.

See this image and copyright information in PMC

References

1. The White House. 2015. National action plan for combatting antibiotic-resistant bacteria. The White House, Washington, DC: https://www.cdc.gov/drugresistance/pdf/national_action_plan_for_combatin....
1. Clinical and Laboratory Standards Institute. 2018. Performance standards for antimicrobial susceptibility testing, 28th ed. CLSI supplement M100 Clinical and Laboratory Standards Institute, Wayne, PA.
1. Nijhuis RH, van Maarseveen NM, van Hannen EJ, van Zwet AA, Mascini EM. 2014. A rapid and high-throughput screening approach for methicillin-resistant Staphylococcus aureus based on the combination of two different real-time PCR assays. J Clin Microbiol 52:2861–2867. doi:10.1128/JCM.00808-14. - DOI - PMC - PubMed
1. Becker K, Denis O, Roisin S, Mellmann A, Idelevich EA, Knaack D, van Alen S, Kriegeskorte A, Kock R, Schaumburg F, Peters G, Ballhausen B. 2016. Detection of mecA- and mecC-positive methicillin-resistant Staphylococcus aureus (MRSA) isolates by the new Xpert MRSA Gen 3 PCR assay. J Clin Microbiol 54:180–184. doi:10.1128/JCM.02081-15. - DOI - PMC - PubMed
1. Sparbier K, Schubert S, Weller U, Boogen C, Kostrzewa M. 2012. Matrix-assisted laser desorption ionization–time of flight mass spectrometry-based functional assay for rapid detection of resistance against beta-lactam antibiotics. J Clin Microbiol 50:927–937. doi:10.1128/JCM.05737-11. - DOI - PMC - PubMed

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[1] The White House. 2015. National action plan for combatting antibiotic-resistant bacteria. The White House, Washington, DC: https://www.cdc.gov/drugresistance/pdf/national_action_plan_for_combatin....

[2] The White House. 2015. National action plan for combatting antibiotic-resistant bacteria. The White House, Washington, DC: https://www.cdc.gov/drugresistance/pdf/national_action_plan_for_combatin....

[3] Clinical and Laboratory Standards Institute. 2018. Performance standards for antimicrobial susceptibility testing, 28th ed. CLSI supplement M100 Clinical and Laboratory Standards Institute, Wayne, PA.

[4] Clinical and Laboratory Standards Institute. 2018. Performance standards for antimicrobial susceptibility testing, 28th ed. CLSI supplement M100 Clinical and Laboratory Standards Institute, Wayne, PA.

[5] Nijhuis RH, van Maarseveen NM, van Hannen EJ, van Zwet AA, Mascini EM. 2014. A rapid and high-throughput screening approach for methicillin-resistant Staphylococcus aureus based on the combination of two different real-time PCR assays. J Clin Microbiol 52:2861–2867. doi:10.1128/JCM.00808-14. - DOI - PMC - PubMed

[6] Nijhuis RH, van Maarseveen NM, van Hannen EJ, van Zwet AA, Mascini EM. 2014. A rapid and high-throughput screening approach for methicillin-resistant Staphylococcus aureus based on the combination of two different real-time PCR assays. J Clin Microbiol 52:2861–2867. doi:10.1128/JCM.00808-14. - DOI - PMC - PubMed

[7] Becker K, Denis O, Roisin S, Mellmann A, Idelevich EA, Knaack D, van Alen S, Kriegeskorte A, Kock R, Schaumburg F, Peters G, Ballhausen B. 2016. Detection of mecA- and mecC-positive methicillin-resistant Staphylococcus aureus (MRSA) isolates by the new Xpert MRSA Gen 3 PCR assay. J Clin Microbiol 54:180–184. doi:10.1128/JCM.02081-15. - DOI - PMC - PubMed

[8] Becker K, Denis O, Roisin S, Mellmann A, Idelevich EA, Knaack D, van Alen S, Kriegeskorte A, Kock R, Schaumburg F, Peters G, Ballhausen B. 2016. Detection of mecA- and mecC-positive methicillin-resistant Staphylococcus aureus (MRSA) isolates by the new Xpert MRSA Gen 3 PCR assay. J Clin Microbiol 54:180–184. doi:10.1128/JCM.02081-15. - DOI - PMC - PubMed

[9] Sparbier K, Schubert S, Weller U, Boogen C, Kostrzewa M. 2012. Matrix-assisted laser desorption ionization–time of flight mass spectrometry-based functional assay for rapid detection of resistance against beta-lactam antibiotics. J Clin Microbiol 50:927–937. doi:10.1128/JCM.05737-11. - DOI - PMC - PubMed

[10] Sparbier K, Schubert S, Weller U, Boogen C, Kostrzewa M. 2012. Matrix-assisted laser desorption ionization–time of flight mass spectrometry-based functional assay for rapid detection of resistance against beta-lactam antibiotics. J Clin Microbiol 50:927–937. doi:10.1128/JCM.05737-11. - DOI - PMC - PubMed

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

Affiliations

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

Authors

Affiliations

Erratum in

Abstract

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References

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