bioRxiv2018
Bacteriophages shape microbial communities by predating on them and by accelerating their adaptation through horizontal gene transfer. The former is the basis of phage therapy, whereas the latter drives the evolution of numerous bacterial pathogens. We present a novel computational approach (eVIVALDI - eco-eVolutionary mIcrobial indiViduAL-baseD sImulations) to study phage-bacteria ecological interactions that integrates a large number of processes, including population dynamics, environmental structure, genome evolution, and phage-mediated horizontal transfer. We validate and illustrate the relevance of the model by focusing on three specific questions: the ecological interactions between bacteria and virulent phage during phage and antibiotic therapy, the role of prophages as competitive weapons, and how autotransduction facilitates bacterial acquisition of antibiotic resistance genes upon lysis of antibiotic resistant competitors. Our model recapitulates experimental and theoretical observations and provides novel insights. In particular, we find that environmental structure has a strong effect on community dynamics and evolutionary outcomes in all three case studies. Strong environmental structure, relative to well-mixed environments and especially if antibiotics are heterogeneously distributed, enhances the rate of acquisition of resistance to both phages and antibiotics, and leads to more accurate predictions of the dynamics of lysogen invasion in the gastrointestinal tract. We predicted the co-existence of invaders and resident lysogens in autotransduction under a range of parameters, and validated this key prediction experimentally. By linking ecological and evolutionary dynamics, our modelling approach sheds light on the factors that influence the dynamics of phage-bacteria interactions. It can also be expanded to put forward novel hypotheses, facilitating the design of phage therapy treatments and the assessment of the role of phages in the spread of antibiotic resistance.\n\nAUTHOR SUMMARYIn the face of a growing threat of antibiotic resistant bacteria, bacteriophages have re-emerged as a potential alternative to clinical treatments of infections, as they are efficient bacterial predators. However, bacteriophages can also promote, through a mechanism called transduction, the dissemination of adaptive traits between bacteria, including antibiotic resistance genes. Importantly, these two types of interactions (predation and transduction) can co-occur, which creates difficulties in predicting their outcome. We have developed eVIVALDI (eco-eVolutionary mIcrobial indiViduAL-baseD sImulations), a computational model that allows the simulation of microbial communities with a focus on the mechanisms involved in phage-bacteria interactions, across time and in different types of environments. eVIVALDI can be used to understand the conditions where phages are more likely to be successfully used to eliminate bacteria or, in the other hand, the conditions where they increase the probability of dissemination of adaptive traits. Our research highlights the importance of considering the diverse ways that phage and bacteria interact, and the relevant ecological conditions where these interactions take place, to understand how bacteriophages shape microbial communities and how they can be used as a clinical tool.