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Machine learning detection of heteroresistance in Escherichia coli
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology, Infection and Immunity.ORCID iD: 0000-0002-6329-2421
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology, Infection and Immunity.ORCID iD: 0000-0003-3326-8495
Department of Biosciences, University of Milan, Milan, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology, Infection and Immunity.
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2025 (English)In: EBioMedicine, E-ISSN 2352-3964, Vol. 113, article id 105618Article in journal (Refereed) Published
Abstract [en]

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.

Methods

We classified 467 Escherichia coli clinical isolates as HR or non-HR to the often used β-lactam/inhibitor combination piperacillin-tazobactam using pre-screening and Population Analysis Profiling tests. We sequenced the isolates, assembled the whole genomes and created a set of predictors based on current knowledge of HR mechanisms. Then we trained several machine learning models on 80% of this data set aiming to detect HR isolates. We compared performance of the best ML models on the remaining 20% of the data set with a baseline model based solely on the presence of β-lactamase genes. Furthermore, we sequenced the resistant sub-populations in order to analyse the genetic mechanisms underlying HR.

Findings

The best ML model achieved 100% sensitivity and 84.6% specificity, outperforming the baseline model. The strongest predictors of HR were the total number of β-lactamase genes, β-lactamase gene variants and presence of IS elements flanking them. Genetic analysis of HR strains confirmed that HR is caused by an increased copy number of resistance genes via gene amplification or plasmid copy number increase. This aligns with the ML model's findings, reinforcing the hypothesis that this mechanism underlies HR in Gram-negative bacteria.

Interpretation

We demonstrate that a combination of WGS and ML can identify HR in bacteria with perfect sensitivity and high specificity. This improved detection would allow for better-informed treatment decisions and potentially reduce the occurrence of treatment failures associated with HR.

Place, publisher, year, edition, pages
2025. Vol. 113, article id 105618
Keywords [en]
Antibiotic resistance, Antibiotic heteroresistance, E. coli, Machine learning, Piperacillin-tazobactam
National Category
Artificial Intelligence Bioinformatics and Computational Biology Microbiology Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-551626DOI: 10.1016/j.ebiom.2025.105618ISI: 001432028800001Scopus ID: 2-s2.0-85217905563OAI: oai:DiVA.org:uu-551626DiVA, id: diva2:1940866
Funder
Swedish Research Council, 2021-02091NIH (National Institutes of Health), U19AI158080-01Available from: 2025-02-27 Created: 2025-02-27 Last updated: 2025-04-18Bibliographically approved
In thesis
1. Heteroresistance - from clinical implications to genetic mechanisms
Open this publication in new window or tab >>Heteroresistance - from clinical implications to genetic mechanisms
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Antibiotic heteroresistance (HR) is a phenotype characterized by the presence of low frequency subpopulations with increased resistance present in a more susceptible main population of bacteria. The clinical impact of the phenotype is not clear, but in vitro, in vivo and clinical studies suggest that HR increases the risk of treatment failure. To further complicate the situation, HR often evades detection by commonly used diagnostic methods. The phenotype can be caused by several different genetic mechanisms, and one main mechanism is tandem gene amplification of resistance genes. In this thesis, questions regarding clinical implications, prevalence, diagnostics and genetic mechanisms are adressed in three papers.

In Paper I, the prevalence, classification and clinical outcomes of breakpoint crossing HR (BCHR) in 255 Escherichia coli bloodstream infection isolates were investigated for three clinically relevant antibiotics in a retrospective study. The BCHR prevalences for cefotaxime, gentamicin and piperacillin-tazobactam were <1%, 43% and 9%, respectively. Out of 125 BCHR isolates 96% (120/125) were classified as suceptible by disk diffusion. Patients with BCHR infections that were treated with the corresponding antibiotic had higher odds for admittance to the intensive care unit and mortality for gentamicin, and for admittance to the intermediate care unit for piperacillin-tazobactam.

In Paper II, we predicted the HR phenotype from whole genome sequencing data utilizing machine learning algorithms. 467 clinical isolates of E. coli phenotyped for piperacillin-tazobactam HR were included. The best performing model detected HR isolates with 100% sensitivity and 84.6% specificity. Genetic analysis of the resistant subpopulations showed that copy number increases of resistance genes, either due to amplifications or increased plasmid copy number, were the main HR mechanisms.

In Paper III, the population distribution of resistance gene tandem amplifications in a HR E. coli isolate was resolved and studied, using a new method combining genetic engineering and Nanopore long read sequencing. The distribution of amplifications correlated with the observed HR phenotype. Mathematical modelling suggested that indirect resistance mechanisms could affect the distribution of amplification copy numbers.  

In conclusion, these findings advance the understanding of the prevalence, clinical outcome, diagnostics and genetic mechanisms of the HR phenotype. The presented methodologies in Paper II and III can aid in developing better diagnostics for detection of HR, and in further investigations of the parameters that shapes the HR population and how these populations impact the clinical outcomes of antibiotic treatment.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2025. p. 48
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 2154
Keywords
Heteroresistance, antibiotic heteroresistance, antibiotic resistance
National Category
Microbiology in the Medical Area
Research subject
Microbiology
Identifiers
urn:nbn:se:uu:diva-554900 (URN)978-91-513-2492-0 (ISBN)
Public defence
2025-06-12, room B41, Biomedical Centre (BMC), Husargatan 3, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2025-05-20 Created: 2025-04-18 Last updated: 2025-05-20

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Guliaev, AndreiHjort, KarinJonsson, SofiaNicoloff, HervéGuy, LionelAndersson, Dan I.

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