Ahmed Babiker1, Mustapha M Mustapha2, Marissa P Pacey2, Kathleen A Shutt3, Chinelo D Ezeonwuka2, Sara L Ohm2, Vaughn S Cooper4, Jane W Marsh2, Yohei Doi5, Lee H Harrison3. 1. Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: ahmed.babiker@emory.edu. 2. Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh, Pittsburgh, PA, USA. 3. Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh, Pittsburgh, PA, USA. 4. Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 5. Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Abstract
OBJECTIVES: The antimicrobial resistance (AMR) crisis represents a serious threat to public health and has resulted in concentrated efforts to accelerate development of rapid molecular diagnostics for AMR. In combination with publicly available web-based AMR databases, whole-genome sequencing (WGS) offers the capacity for rapid detection of AMR genes. Here we studied the concordance between WGS-based resistance prediction and phenotypic susceptibility test results for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) clinical isolates using publicly available tools and databases. METHODS: Clinical isolates prospectively collected at the University of Pittsburgh Medical Center between December 2016 and December 2017 underwent WGS. The AMR gene content was assessed from assembled genomes by BLASTn search of online databases. Concordance between the WGS-predicted resistance profile and phenotypic susceptibility as well as the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for each antibiotic/organism combination, using the phenotypic results as gold standard. RESULTS: Phenotypic susceptibility testing and WGS results were available for 1242 isolate/antibiotic combinations. Overall concordance was 99.3%, with a sensitivity, specificity, PPV and NPV of 98.7% (95% CI 97.2-99.5%), 99.6% (95% CI 98.8-99.9%), 99.3% (95% CI 98.0-99.8%) and 99.2% (95% CI 98.3-99.7%), respectively. Additional identification of point mutations in housekeeping genes increased the concordance to 99.4%, sensitivity to 99.3% (95% CI 98.2-99.8%) and NPV to 99.4% (95% CI 98.4-99.8%). CONCLUSION: WGS can be used as a reliable predicator of phenotypic resistance both for MRSA and VRE using readily available online tools.
OBJECTIVES: The antimicrobial resistance (AMR) crisis represents a serious threat to public health and has resulted in concentrated efforts to accelerate development of rapid molecular diagnostics for AMR. In combination with publicly available web-based AMR databases, whole-genome sequencing (WGS) offers the capacity for rapid detection of AMR genes. Here we studied the concordance between WGS-based resistance prediction and phenotypic susceptibility test results for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) clinical isolates using publicly available tools and databases. METHODS: Clinical isolates prospectively collected at the University of Pittsburgh Medical Center between December 2016 and December 2017 underwent WGS. The AMR gene content was assessed from assembled genomes by BLASTn search of online databases. Concordance between the WGS-predicted resistance profile and phenotypic susceptibility as well as the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for each antibiotic/organism combination, using the phenotypic results as gold standard. RESULTS: Phenotypic susceptibility testing and WGS results were available for 1242 isolate/antibiotic combinations. Overall concordance was 99.3%, with a sensitivity, specificity, PPV and NPV of 98.7% (95% CI 97.2-99.5%), 99.6% (95% CI 98.8-99.9%), 99.3% (95% CI 98.0-99.8%) and 99.2% (95% CI 98.3-99.7%), respectively. Additional identification of point mutations in housekeeping genes increased the concordance to 99.4%, sensitivity to 99.3% (95% CI 98.2-99.8%) and NPV to 99.4% (95% CI 98.4-99.8%). CONCLUSION: WGS can be used as a reliable predicator of phenotypic resistance both for MRSA and VRE using readily available online tools.
Authors: Andrew G McArthur; Nicholas Waglechner; Fazmin Nizam; Austin Yan; Marisa A Azad; Alison J Baylay; Kirandeep Bhullar; Marc J Canova; Gianfranco De Pascale; Linda Ejim; Lindsay Kalan; Andrew M King; Kalinka Koteva; Mariya Morar; Michael R Mulvey; Jonathan S O'Brien; Andrew C Pawlowski; Laura J V Piddock; Peter Spanogiannopoulos; Arlene D Sutherland; Irene Tang; Patricia L Taylor; Maulik Thaker; Wenliang Wang; Marie Yan; Tennison Yu; Gerard D Wright Journal: Antimicrob Agents Chemother Date: 2013-05-06 Impact factor: 5.191
Authors: K Schmidt; S Mwaigwisya; L C Crossman; M Doumith; D Munroe; C Pires; A M Khan; N Woodford; N J Saunders; J Wain; J O'Grady; D M Livermore Journal: J Antimicrob Chemother Date: 2016-09-25 Impact factor: 5.790
Authors: Scott A Cunningham; Patricio R Jeraldo; Audrey N Schuetz; Angela A Heitman; Robin Patel Journal: Diagn Microbiol Infect Dis Date: 2020-04-11 Impact factor: 2.803
Authors: Tianchi Chen; Lin Zhao; Yao Liu; Ya'nan Wang; Ying Jian; Na Zhao; Ziyu Yang; Xi Wang; Qian Liu; Min Li Journal: J Antimicrob Chemother Date: 2022-09-30 Impact factor: 5.758
Authors: Emelia H Adator; Matthew Walker; Claudia Narvaez-Bravo; Rahat Zaheer; Noriko Goji; Shaun R Cook; Lisa Tymensen; Sherry J Hannon; Deirdre Church; Calvin W Booker; Kingsley Amoako; Celine A Nadon; Ron Read; Tim A McAllister Journal: Microorganisms Date: 2020-03-22