Machine learning detection of heteroresistance in Escherichia coli.

Journal: EBioMedicine
PMID: 39986174

Abstract

BACKGROUND: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, animal experiments and clinical studies associate HR with treatment failure. Currently used susceptibility tests do not detect heteroresistance reliably, which can result in misclassification of heteroresistant isolates as susceptible which might lead to treatment failure. Here we examined if whole genome sequence (WGS) data and machine learning (ML) can be used to detect bacterial HR.

Authors

  • Andrei Guliaev
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
  • Karin Hjort
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
  • Michele Rossi
  • Sofia Jonsson
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
  • Hervé Nicoloff
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
  • Lionel Guy
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden; SciLifeLab, Uppsala University, Uppsala, Sweden.
  • Dan I Andersson
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. Electronic address: Dan.Andersson@imbim.uu.se.

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