Editors' ChoiceANTIMICROBIAL RESISTANCE

Who’s afraid of the big bad pathogen?

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Science Translational Medicine  06 Dec 2017:
Vol. 9, Issue 419, eaar4428
DOI: 10.1126/scitranslmed.aar4428

Abstract

Bacterial predation by Acinetobacter baylyi substantially increases cross-species horizontal gene transfer, leading to rapid acquisition of antimicrobial resistance genes.

Infections due to multidrug-resistant (MDR) pathogens, or “super bugs,” are on the rise worldwide. The gram-negative bacteria belonging to Acinetobacter are particularly concerning pathogens. Acinetobacter baumanii is a major culprit for hospital-acquired lung infections and exhibits high rates of antimicrobial resistance (AMR) gene acquisition, limiting the arsenal of effective treatments. Acinetobacter has been shown to acquire AMR through horizontal gene transfer (HGT), in which genes are obtained from other pathogens. However, how these AMR genes are transferred at such high rates is poorly understood.

Cooper et al. studied HGT in Acinetobacter baylyi, an easily manipulated and well-studied close relative of A. baumanii. They show that A. baylyi speeds up the process of AMR gene acquisition through bacterial predation, in which neighboring unrelated bacteria are killed in a contact-dependent manner, and released DNA is stolen from these lysed cells. The authors grew Escheria coli containing green florescence and AMR genes with A. baylyi in a microfluidics system that allows for visualization of cell dynamics at a single-cell level. As the Acinetobacter and E. coli began to grow together, they visualized lysis of E. coli cells. Some Acinetobacter cells also began to fluoresce green, indicating HGT. When the antibiotic to which the E. coli was resistant was added to the model, the green-fluorescing Acinetobacter cells continued to grow, indicating AMR gene acquisition. When killing was disabled in Acinetobacter cells through replacement of a required gene, HGT of AMR genes from E. coli was drastically reduced.

The authors next used a computational model to simulate the population dynamics of killing-enhanced HGT. Initial density of bacteria, ratio of predator to prey bacteria, and time of contact all affected HGT rates. These rates were fastest when there was a high predator and low prey density, particularly immediately after the start of experiments.

This study contributes to our understanding of how Acinetobacter acquires AMR at such high rates. The presented computational model provides an approach to study the environments that promote or inhibit this killing-enhanced HGT. Their work will need to be extended to other pathogens to understand disparities. However, these findings are a step in the right direction in advancing our understanding of how to combat MDR.

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