Managing the emergence of pathogen resistance via spatially targeted antimicrobial use

Advancing phage therapy through the lens of virus host-breadth and emergence potential

Phages are viruses that specifically infect bacteria, and their biodiversity contributes to historical and current development of phage therapy to treat myriad bacterial infections. Phage therapy holds promise as an alternative to failing chemical antibiotics, but there are benefits and costs of this technology. Here, we review the rich history of phage therapy, highlighting reasons (often political) why it was widely rejected by Western medicine until recently. One longstanding idea involves mixing different phages together in cocktails, to increase the probability of killing target pathogenic bacteria without pre-screening for phage susceptibility. By challenging 30 lytic phages to infect 14 strains of the bacteria Pseudomonas aeruginosa, we showed that some phages were "generalists" with broad host-ranges, emphasizing that extreme host-specificity of phages was not necessarily a liability. Using a "greedy algorithm" analysis, we identified the best cocktail mixture of phages to achieve broad bacteria killing. Additionally, we review how virus host-range can evolve and connect lessons learned from virus emergence-including contributions of elevated virus mutation rates in promoting emergence and virus evolutionary transitions from specialized to generalized host-use-as cautionary tales for avoiding risk of "off-target" phage emergence on commensal bacteria in microbiomes. Throughout, we highlight how fundamental understanding of virus ecology and evolution is vital for developing phage therapy; heeding these principles should help in designing therapeutic strategies that do not recapitulate consequences of virus selection to emerge on novel hosts.

Diffusion-driven enhancement of the antibiotic resistance selection window

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Managing the emergence of pathogen resistance via spatially targeted antimicrobial use

From agriculture to public health to civil engineering, managing antimicrobial resistance presents a considerable challenge. The dynamics underlying resistance evolution reflect inherently spatial processes. Resistant pathogen strains increase in frequency when a strain that emerges in one locale can spread and replace pathogen subpopulations formerly sensitive to the antimicrobial agent. Moreover, the strength of selection for antimicrobial resistance is in part governed by the extent of antimicrobial use. Thus, altering how antimicrobials are used across a landscape can potentially shift the spatial context governing the dynamics of antimicrobial resistance and provide a potent management tool. Here, we model how the efficacy of adjusting antimicrobial use over space to manage antimicrobial resistance is mediated by competition among pathogen strains and the topology of pathogen metapopulations. For several pathogen migration scenarios, we derive critical thresholds for the spatial extent of antimicrobial use below which resistance cannot emerge, and relate these thresholds to (a) the ability to eradicate antimicrobial-sensitive pathogens locally and (b) the strength of the trade-off between resistance ability and competitive performance where antimicrobial use is absent. We find that in metapopulations where patches differ in connectedness, constraining antimicrobial use across space to mitigate resistance evolution only works if the migration of the resistant pathogen is modest; yet, this situation is reversed if the resistant strain has a high colonization rate, with variably connected metapopulations exhibiting less sensitivity to reducing antimicrobial use across space. Furthermore, when pathogens are alternately exposed to sites with and without the antimicrobial, bottlenecking resistant strains through sites without an antimicrobial is only likely to be effective under a strong competition-resistance trade-off. We therefore identify life-history constraints that are likely to suggest which pathogens can most effectively be controlled by a spatially targeted antimicrobial regime. We discuss implications of our results for managing and thinking about antimicrobial resistance evolution in spatially heterogeneous contexts.