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Updated: Jul 23, 2025

Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach

Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach

Published on: December 14, 2019

Mge-cluster: a reference-free approach for typing bacterial plasmids.

Sergio Arredondo-Alonso^1,2, Rebecca A Gladstone¹, Anna K Pöntinen^1,3

¹Department of Biostatistics, University of Oslo, Oslo, Norway.

NAR Genomics and Bioinformatics

|July 12, 2023

Summary

This summary is machine-generated.

A new computational tool, mge-cluster, efficiently classifies bacterial plasmids using de Bruijn graphs. This method aids in tracking plasmid evolution and antimicrobial resistance gene spread, like mcr-1.1 in E. coli.

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Area of Science:

Bacterial genomics and evolution
Computational biology and bioinformatics
Antimicrobial resistance surveillance

Background:

Plasmids are crucial extrachromosomal elements driving bacterial evolution and adaptation.
Advancements in long-read sequencing enable high-resolution population-wide plasmid analysis.
Existing plasmid typing methods have limitations in scope and efficiency.

Purpose of the Study:

To develop a computationally efficient tool for simultaneous classification and novel type recognition of plasmids.
To provide an intuitive and interactive platform for plasmid analysis, visualization, and clustering.
To enable consistent plasmid labeling across diverse sequence collections.

Main Methods:

Development of mge-cluster, a novel algorithm utilizing unitig representation in a de Bruijn graph.
Compression of thousands of plasmid sequences for efficient processing.
Implementation of interactive visualization, classification, and clustering functionalities.

Main Results:

mge-cluster demonstrates faster runtime and moderate memory usage compared to existing algorithms.
The platform facilitates easy distribution, replication, and consistent plasmid labeling.
Analysis of an Escherichia coli dataset revealed insights into mcr-1.1 prevalence and hospital-acquired resistance plasmid transmission.

Conclusions:

mge-cluster offers a significant advancement in scalable and efficient plasmid analysis.
The tool supports robust surveillance of antimicrobial resistance genes and plasmid dynamics.
This platform enhances our understanding of plasmid evolution and epidemiology in bacterial populations.