Search for: All records

ABSTRACT Infectious diseases remain a major cause of global mortality, yet basic questions concerning the relationship between within-host processes governing pathogen burden (pathogen replication, immune responses) and population-scale (epidemiological) patterns of mortality remain obscure. We use a structured literature review to leverage the extensive biomedical data generated by controlled host infections to address the epidemiological question of whether infection-induced mortality is constant, accelerating, or follows some other pattern of change and to infer the within-host mechanistic basis of this pattern. We show that across diverse lethal infection models, the risk of death increases approximately exponentially in time since infection, in a manner phenomenologically similar to the dynamics of all-cause death. We further show that this pattern of accelerating risk is consistent with multiple alternate mechanisms of pathogen growth and host-pathogen interaction, underlining the limitations of current experimental approaches to connect within-host processes to epidemiological patterns. We review critical experimental questions that our work highlights, requiring additional non-invasive data on pathogen burden throughout the course of infection.IMPORTANCEHere, we ask a simple question: what are the dynamics of pathogen-induced death? Death is a central phenotype in both biomedical and epidemiological infectious disease biology, yet very little work has attempted to link the biomedical focus on pathogen dynamics within a host and the epidemiological focus on populations of infected hosts. To systematically characterize the dynamics of death in controlled animal infections, we analyzed 209 data sets spanning diverse lethal animal infection models. Across experimental models, we find robust support for an accelerating risk of death since the time of infection, contrasting with conventional epidemiological models that assume a constant elevated risk of death. Using math models, we show that multiple processes of growth and virulence are consistent with accelerating risk of death, and we end with a discussion of critical experiments to resolve how within-host biomedical processes map onto epidemiological patterns of disease. 

Where there are bacteria, there will be bacteriophages. These viruses are known to be important players in shaping the wider microbial community in which they are embedded, with potential implications for human health. On the other hand, bacteria possess a range of distinct immune mechanisms that provide protection against bacteriophages, including the mutation or complete loss of the phage receptor, and CRISPR-Cas adaptive immunity. While our previous work showed how a microbial community may impact phage resistance evolution, little is known about the inverse, namely how interactions between phages and these different phage resistance mechanisms affect the wider microbial community in which they are embedded. Here, we conducted a 10-day, fully factorial evolution experiment to examine how phage impact the structure and dynamics of an artificial four-species bacterial community that includes eitherPseudomonas aeruginosawild-type or an isogenic mutant unable to evolve phage resistance through CRISPR-Cas. Additionally, we used mathematical modelling to explore the ecological interactions underlying full community behaviour, as well as to identify general principles governing the impacts of phage on community dynamics. Our results show that the microbial community structure is drastically altered by the addition of phage, withAcinetobacter baumanniibecoming the dominant species andP.aeruginosabeing driven nearly extinct, whereasP.aeruginosaoutcompetes the other species in the absence of phage. Moreover, we find that aP.aeruginosastrain with the ability to evolve CRISPR-based resistance generally does better when in the presence ofA.baumannii, but that this benefit is largely lost over time as phage is driven extinct. Finally, we show that pairwise data alone is insufficient when modelling our microbial community, both with and without phage, highlighting the importance of higher order interactions in governing multispecies dynamics in complex communities. Combined, our data clearly illustrate how phage targeting a dominant species allows for the competitive release of the strongest competitor while also contributing to community diversity maintenance and potentially preventing the reinvasion of the target species, and underline the importance of mapping community composition before therapeutically applying phage.