Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and...

Area of Science:

• Clinical microbiology
• Genomics
• Infectious disease epidemiology

Background:

• Whole-genome sequencing (WGS) is increasingly used for infectious disease outbreak investigations and predicting antimicrobial susceptibility.
• Multidrug-resistant Acinetobacter baumannii is a common cause of healthcare-associated infections and outbreaks.
• Traditional typing methods are being replaced by more advanced genomic techniques.

Purpose of the Study:

• To evaluate core genome multilocus sequence typing (cgMLST) for Acinetobacter baumannii outbreak investigation.
• To assess the accuracy of genomic data in predicting antimicrobial susceptibility for A. baumannii.
• To compare cgMLST results with previous PCR-electrospray ionization mass spectrometry (PCR/ESI-MS) typing.

Main Methods:

• Analysis of 72 Acinetobacter baumannii isolates using a clinical laboratory workflow for cgMLST.
• Genomic susceptibility prediction performed using the ARESdb platform.
• Comparison of cgMLST data with prior PCR/ESI-MS typing results from the Antimicrobial Resistance Leadership Group (ARLG).

Main Results:

• cgMLST showed strong correlation with previous PCR/ESI-MS typing, with 78% and 94% agreement at different allelic difference thresholds (≤9 and ≤200).
• Genotypic prediction of antimicrobial susceptibility demonstrated 89% categorical agreement with phenotypic testing across 11 drugs.
• Error rates for susceptibility prediction were minor (8%), major (11%), and very major (1%).

Conclusions:

• cgMLST is a robust method for investigating Acinetobacter baumannii outbreaks and complements traditional typing methods.
• Genomic data, analyzed via platforms like ARESdb, can reliably predict antimicrobial susceptibility in A. baumannii.
• These genomic approaches enhance the ability to track and manage multidrug-resistant bacterial infections in healthcare settings.