Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and Antimicrobial Susceptibility Prediction in ARESdb.
Madiha FidaScott A CunninghamStephan BeiskenAndreas E PoschNicholas ChiaPatricio R JeraldoMatthew P MurphyNicole M ZinsmasterRobin Patelnull nullPublished in: Journal of clinical microbiology (2022)
Whole-genome sequencing (WGS) is rapidly replacing traditional typing methods for the investigation of infectious disease outbreaks. Additionally, WGS data are being used to predict phenotypic antimicrobial susceptibility. Acinetobacter baumannii, which is often multidrug-resistant, is a significant culprit in outbreaks in health care settings. A well-characterized collection of A. baumannii was studied using core genome multilocus sequence typing (cgMLST). Seventy-two isolates previously typed by PCR-electrospray ionization mass spectrometry (PCR/ESI-MS) provided by the Antimicrobial Resistance Leadership Group (ARLG) were analyzed using a clinical microbiology laboratory developed workflow for cgMLST with genomic susceptibility prediction performed using the ARESdb platform. Previously performed PCR/ESI-MS correlated with cgMLST using relatedness thresholds of allelic differences of ≤9 and ≤200 allelic differences in 78 and 94% of isolates, respectively. Categorical agreement between genotypic and phenotypic antimicrobial susceptibility across a panel of 11 commonly used drugs was 89%, with minor, major, and very major error rates of 8%, 11%, and 1%, respectively.
Keyphrases
- acinetobacter baumannii
- infectious diseases
- multidrug resistant
- ms ms
- mass spectrometry
- genetic diversity
- antimicrobial resistance
- drug resistant
- pseudomonas aeruginosa
- gram negative
- healthcare
- multiple sclerosis
- liquid chromatography
- klebsiella pneumoniae
- electronic health record
- real time pcr
- copy number
- amino acid
- high resolution
- genome wide
- gas chromatography
- capillary electrophoresis
- dna methylation
- gene expression
- health insurance
- artificial intelligence