Impacts of Collateral Effects and Spatial Heterogeneity on the Evolution of Resistance in Enterococcus

Abstract

Antibiotics are used to treat bacterial infections, as they can kill or inhibit the growth of bacterial populations. However, the misuse of antibiotics may also promote the evolution of drug resistance, reducing the efficacy of many treatments. The rapid rise of antibiotic resistance continues to outpace the development of new drugs and is becoming one of the most significant global health challenges. The mechanisms of antibiotic resistance are increasingly understood at the molecular level. But understanding how the dynamics of bacterial communities shape the emergence of resistance in microbial populations is an ongoing challenge. In this thesis, we combine quantitative experiments on bacteria with simple mathematical models to investigate how resistance to antibiotic emerges in communities of E. faecalis, a Gram positive bacterial species and opportunistic human pathogen. Our focus is not on identifying new mechanisms of drug resistance; instead, our goal is to gain a deeper understanding of how mutants harboring known resistance mechanisms rise to dominance in microbial populations characterized by temporal and spatial heterogeneity.

This thesis can be divided into two primary themes: time dependent effects of collateral evolution (Part I) and effects of multi-strain interactions in heterogeneous communities (Part II). Collateral effects refer to an increase (collateral resistance) or decrease (collateral sensitivity) in resistance to one drug that occurs during adaptation to a second drug. In Part I of this work, we use laboratory evolution experiments to investigate how collateral sensitivity profiles change over time as E. faecalis undergoes adaptation to a diverse library of antibiotics. We describe a rich collection of dynamics that exhibit global trends--for example, collateral resistance often arises in early stages of adaptation, while sensitivity tends to increase in later stages--but also reveal a number of drug- and population-specific dynamics that we characterize using a combination of genome sequencing and phenotype measurements.

In Part II, we investigate how interactions between cells and the surrounding spatial environment influence the evolution of resistance. Our work focuses on two aspects of these interactions: the role of 1) spatial heterogeneity and 2) inter-cellular competition in modulating the evolution of resistance. We investigate these issues in laboratory populations using customized, computer-controlled bioreactors, which allow us to experimentally simulate both migration dynamics between spatially distinct populations and adaptive antibiotic treatments that depend on cell density. Our findings reveal a complex interplay between migration, spatial heterogeneity, and population density, demonstrating how different features of the environment can accelerate, or impede, the evolution of resistance.

As a whole, this thesis highlights both the simplicity and complexity of evolutionary dynamics leading to resistance. On one hand, Part I highlights how collateral effects and evolution can be remarkably diverse even in simple laboratory settings. Part II underscores the important role that spatial heterogeneity can play in modulating resistance, yet also highlights how initially counterintuitive dynamics can--at least sometimes--be understood with simple mathematical models. We hope these results motivate continued explorations of resistance evolution, both in the lab and the clinic.