Quantifying plasmid movement in drug-resistant Shigella species using phylodynamic inference

Area of Science:

• Microbiology
• Evolutionary Biology
• Computational Biology

Background:

• Antimicrobial resistance (AMR) is a growing global health crisis.
• AMR genes are frequently located on plasmids, mobile genetic elements that can transfer between bacteria.
• Quantifying plasmid movement and AMR gene dissemination is computationally challenging.

Purpose of the Study:

• To introduce a novel computational method for reconstructing and quantifying plasmid movement in bacterial populations.
• To model the co-evolution of chromosomal and plasmid DNA to infer plasmid transfer events.
• To analyze AMR plasmid dynamics in Shigella populations over a five-year period.

Main Methods:

• Developed a Bayesian phylogenetic network approach modeling joint chromosomal and plasmid DNA co-evolution.
• Incorporated a coalescent process and a plasmid transfer model to infer evolutionary histories.
• Applied the method to a five-year Shigella dataset, analyzing five plasmids with varying AMR and virulence profiles.

Main Results:

• Reconstructed the co-evolution of a large Shigella virulence plasmid with chromosomal DNA.
• Quantified higher transfer rates for three small plasmids moving between Shigella sonnei lineages.
• Identified recent, independent dissemination events of a multidrug-resistant plasmid between S. sonnei and S. flexneri.

Conclusions:

• The novel method accurately reconstructs plasmid movement and transfer rates between bacterial lineages.
• This approach enhances understanding of AMR-carrying plasmid dynamics, including introduction, circulation, and maintenance.
• Findings highlight specific plasmid transfer events contributing to the spread of multidrug resistance in Shigella.