Significance: This study explores the potential of a phage, PYO ^Sa , for treating Staphylococcus aureus infections in combination with antibiotics. Population dynamic and genomic analysis identified a limitation and potential liability of using PYO ^Sa for therapy. Due to the production of potentially pathogenic atypical small colony variants, PYO ^Sa alone cannot eliminate S. aureus populations. However, we demonstrate that by following the administration of PYO ^Sa with bactericidal antibiotics, this limitation and potential liability can be addressed. The methods used in this investigation to explore the efficacy of combinations of PYO ^Sa and antibiotics for treating S. aureus  infections can be employed to evaluate the clinical potential and facilitate the design of treatment protocols for any bacteria and phage that can be cultured in vitro.

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

Fueled by the increasing abundance of antibiotic-resistant pathogens, there has been a resurrection in the use of bacterial viruses (bacteriophages or 'phage') for therapeutic applications. Phage therapy was used in the early 20th century to limited success, which we attribute to its haphazard employment. To avoid repeating the mistakes of the past, this Opinion first evaluates the historical reasons for the failure of phage therapy, analyzes the current state of the field, and ultimately makes recommendations for how to proceed with contemporary phage therapy. Despite many advances in phage biology, crucial gaps in our knowledge persist. Our recommendations require physicians, scientists, and public-policy leaders to cooperate to bridge the outstanding gaps around phage therapy to develop phage into a useful therapeutic tool.

Antibiotics are considered one of the most important contributions to clinical medicine in the last century. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulation models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend that the definition of heteroresistance should include a consideration of the rate of return to susceptibility.

The rational design of the antibiotic treatment of bacterial infections employs these drugs to reach concentrations that exceed the minimum needed to prevent the replication of the target bacteria. However, within a treated patient, spatial and physiological heterogeneity promotes antibiotic gradients such that the concentration of antibiotics at specific sites is below the minimum needed to inhibit bacterial growth. Here, we investigate the effects of sub-inhibitory antibiotic concentrations on three parameters central to bacterial infection and the success of antibiotic treatment, using in vitro experiments with Staphylococcus aureus and mathematical and computer-simulation models. Our results, using drugs of six different classes, demonstrate that exposure to sub-inhibitory antibiotic concentrations alters bacterial growth dynamics, increases the mutation rate to antibiotic resistance, and decreases the production of persister cells thereby reducing persistence levels. Understanding this trade-off between mutation rates and persistence levels resulting from sub-inhibitory antibiotic exposure is crucial for optimizing, and mitigating the failure of, antibiotic therapy. IMPORTANCE: Much of the research on antibiotics and antibiotic treatment has focused on drug concentrations sufficient to prevent the growth of bacteria. These concentrations, however, are not always reached everywhere in the body. Here, we look at the effects of exposure to these low concentrations of antibiotics on the common clinically important pathogen Staphylococcus aureus . We confirm a previous finding that sub-inhibitory antibiotic exposure decreases the total growth and the growth rate of the bacteria. Moreover, we demonstrate that the level of persistence, an important mechanism for bacteria to survive antibiotics, is decreased due to sub-inhibitory exposure. However, we find that the rate of generation of resistant mutants is substantially increased. Taken together, these results reveal an important trade-off that emerges as a consequence of bacteria being exposed to sub-inhibitory concentrations of antibiotics.

Critical to our understanding of infections and their treatment is the role the innate immune system plays in controlling bacterial pathogens. Nevertheless, many in vivo systems are made or modified such that they do not have an innate immune response. Use of these systems denies the opportunity to examine the synergy between the immune system and antimicrobial agents. In this study, we demonstrate that the larva of Galleria mellonella is an effective in vivo model for the study of the population and evolutionary biology of bacterial infections and their treatment. To do this, we test three hypotheses concerning the role of the innate immune system during infection. We show that i) sufficiently high densities of bacteria are capable of saturating the innate immune system, ii) bacteriostatic drugs and bacteriophages are as effective as bactericidal antibiotics in preventing mortality and controlling bacterial densities, and iii) minority populations of bacteria resistant to a treating antibiotic will not ascend. Using a highly virulent strain of Staphylococcus aureus and a mathematical computer-simulation model, we further explore how the dynamics of the infection within the short term determine the ultimate infection outcome. We find that immune activation in response to high densities of bacteria leads to a strong but short-lived immune response which ultimately results in a high degree of mortality. Overall, our findings illustrate the utility of the G. mellonella model system in conjunction with established in vivo models in studying infectious disease progression and treatment.