RT Journal Article
SR Electronic
T1 Modelling bacteria-phage interactions driving predation and horizontal gene transfer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 291328
DO 10.1101/291328
A1 Jorge A. Moura de Sousa
A1 Ahlam Alsaadi
A1 Jakob Haaber
A1 Hanne Ingmer
A1 Eduardo P.C. Rocha
YR 2018
UL http://biorxiv.org/content/early/2018/03/30/291328.abstract
AB 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 bacteria-phage 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 in the context of phage and antibiotic therapy, the role of prophages as competitive weapons, and autotransduction leading to bacterial acquisition of antibiotic resistance genes upon lysis of 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, especially if antibiotics are heterogeneously distributed, promotes the acquisition of resistance to both phages and antibiotics, creates variation in the dynamics of arm-races between bacteria and phage, and better predicts dynamics of lysogen invasion in the gastrointestinal tract, compared to models assuming well-mixed environments. Moreover, we predict a parameter space where co-existence between invaders and resident lysogens can occur during autotransduction, which we then confirm experimentally. By linking ecological and evolutionary dynamics, our modelling approach sheds light on the factors that influence the dynamics of bacteria-phage 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.