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Title: Modelling within-Host Spatiotemporal Dynamics of Invasive Bacterial Disease}
Article:
Abstract:
Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations( wild-type isogenic tagged strains[WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host–pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host–pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics.
Introduction:
Central to a complete understanding of any disease is the ability to integrate information from different scales into a coherent model that fully explains the disease process[1]. One challenge that remains is how to move from our ever more detailed understanding of cellular and molecular microbiology in artificial laboratory systems, towards an explanation of the dynamics of pathogen survival and growth in a whole-animal infection model[2]. A promising approach relies on concepts and mathematical models from population ecology to derive quantitative information on within-host bacterial dynamics from experimental data[3, 4]. However, development of an appreciation of within-host dynamics has been hampered by the difficulty of identifying and observing directly, within tissues, the multiple key variables that underlie the infection process. Numerical variations in total microbial load in bacterial infections and the effects of host factors or therapeutic intervention on these variations are usually followed in vivo by counting the number of colony-forming units( CFUs) present in infected organs and plotting this through time. This provides an overarching idea of the global numerical variation in the bacterial population in the organ as a whole, but gives no information about the fine structure of the population in vivo. At one extreme, all bacteria might display identical growth in all infected cells. At the other extreme, only one clone of bacteria might be growing very rapidly, with all others not growing or growing very slowly. Both these scenarios could lead to the same overall growth dynamics at the organ level. However, the biology underlying them would be very different, having major consequences for understanding the most appropriate way to manipulate the immune system and/or antimicrobial drug regimes to combat these infections. Attempts have been made to measure the actual division rate of bacteria in vivo( reviewed by[2]): they typically rely on introducing nonreplicating elements( e. g., phages, plasmids, or temperature-sensitive mutants) into the bacteria. However, their widespread use has been hindered by the sensitivity of foreign elements to experimental conditions. In addition, the extent to which those methods disrupt the phenotype of bacteria and the immune response has not been assessed. The aim of this study was to obtain a comprehensive picture of in vivo bacterial population dynamics by unravelling the variations in basic demographic processes that drive spatiotemporal events. To this aim, it was necessary to use( 1) a host–pathogen system already established as a model for bacterial dynamics,( 2) a reliable marker for bacterial division or death that does not modify the infection, and( 3) an analytical platform that allowed us to elicit information about unobserved processes from observed data. In this study, we used Salmonella enterica serovar Typhimurium infections in mice, a well-documented, genetically tractable system and a natural host–pathogen interaction with defined infection dynamics amenable to robust statistical analysis. S. enterica infections of mice are a good model for human typhoid fever, for other invasive salmonelloses, and for infections with intracellular pathogens in general. We report an integrated approach to investigate bacterial growth dynamics over the first few days of an acute murine Salmonella infection, which relies on tracing tagged subpopulations of otherwise identical bacteria through infected animals and their organs. Previous studies[5–7] have exploited coinfections of single hosts with several strains of a given pathogen as a powerful tool to explore population dynamics during infection; however, such studies are limited by three main factors: the number of strains that can be distinguished inside the host, the limit of detection/discrimination of strains, and the potential phenotypic variations associated with genetic diversity. To overcome these limitations, we generated eight wild-type isogenic tagged strains( WITS); phenotypically identical bacterial strains each carrying a different 40-bp DNA signature tag in the same noncoding region of the chromosome( Figure 1A) with no interstrain difference in in vitro or in vivo growth rates. Using inocula consisting of mixtures of low numbers of each tagged strain, we monitored the dynamics of each subpopulation at different body sites throughout the course of a systemic infection. We address three specific questions about the dynamics of infection: how does an acute bacterial infection replicate and grow in infected organs? Does the spread of infection within and between organs result from homogeneous bacterial growth, or from expansion of subpopulations? How do components of the immune response affect spatiotemporal bacterial population dynamics? We used depletion of tagged diversity as a proxy for bacterial death, and spatiotemporal heterogeneity as a proxy for bacterial transfer between organs as a function of time postinoculation. This allowed the development of mathematical models to dissect the determinants of net growth at different infection stages, and the mapping of local( intraorgan) versus systemic spread of each bacterial subpopulation. This work illustrates that to understand the basic idiosyncrasies of infection requires a disaggregation of the global disease dynamics and awareness of the separate infection dynamics of subpopulations within the same host. This approach is suitable for many animal infection models; evaluating infection dynamics in these terms will provide a paradigm shift in our understanding of the whole animal infection process.
Discussion:
The course and outcome of a S. enterica infection depends upon many host and bacterial factors, and a number of models have been proposed to describe the infection process: the hypothesis of independent action of pathogens[12], the birth–death model[13], and the two-stage model of microbial infection[14]. Results from numerous studies over the last 50 y indicate that the progression of murine salmonellosis can be divided into two different phases, an early phase of exponential Salmonella growth in the spleen and liver, and a later septic phase that precedes death[15, 16]. The onset of these phases differs according to variations in experimental design and differences in bacterial and host strain. Bacterial growth, death, and spread in the body are still undetermined aspects of the infection process despite their exquisite relevance to vaccination and treatment. Studies often focus on observation of the disease course at the whole-animal level or in whole organs, and interpret the gross, cumulative effects of many individual interactions and use the mean to represent what is a complex phenomenon. However, an appreciation of population dynamics at the gross level provides almost no information on where individual bacteria locate and how they spread and interact during infection. Simply assessing bacterial load in each animal by plate count is not sufficient or sophisticated enough to allow us to fully understand the infection process. Spatial and functional independence between individual bacterial populations in the same host has been conclusively demonstrated by several studies from our group. Previously, we have shown that during a systemic S. enterica infection, each infectious focus is a separate unit resulting from clonal growth of an individual bacterium, with spatial segregation of bacterial populations and continuous distribution to new phagocytes throughout the infection process[4, 5, 17]. Thus, understanding the overall dynamics of an infectious disease hinges on the elucidation of the complexity and dimensionality of functional independence and heterogeneity at the subpopulation level in the animal. In this study, we have used an infection model based on simultaneous administration of tagged subpopulations of otherwise identical bacteria( WITS) in the same animal, to resolve the difficult question of how components of the host immune responses counteract the net growth of the bacterial load in the tissues and to determine the relevance of bactericidal and bacteriostatic mechanisms in these processes. We have shown that shortly after infection, concomitant NADPH oxidase-dependent death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs. Subsequently, a reduction in bacterial mortality leads to an exponential increase in the number of bacteria that spread locally and independently within each organ( e. g., spleen and liver). During the later stages of infection, different bacterial populations mix between organs via the blood with a further stochastic selection of bacterial subpopulations. Populations of genetically identical bacterial strains are a very powerful tool to address novel questions on the nature of individual events that determine the global profile of population dynamics during infection. Having developed and validated the use of WITS in the study of the in vivo population biology of S. enterica, we are now in the position to exploit these systems to great effect to understand the impact of key host and bacterial factors on the pathogenesis of S. enterica at the level of individual bacterial subpopulations. This also allows us to address the extent to which we can rely on data generated in in vitro cell biology systems to describe what is actually happening in vivo. Understanding the effect of anatomical bottlenecks and individual components of the immune system on local and bacteraemic spread is fundamental in order to tailor appropriate prevention and treatment strategies to different stages and forms of S. enterica diseases in animals and man. The specific model we have chosen has focused on S. enterica, but it is an approach that can be applied to the study of the population dynamics of many infectious animal diseases. The use of tagged wild-type strains has the potential to transform our understanding of within-host dynamics of pathogen interactions with host cells, to provide the information needed to build mechanistic mathematical models, generate new research hypotheses, and make quantitative predictions. For example, by using combinations of WITS in animals from different genetic backgrounds, in gene-targeted immunodeficient animals, or in previously vaccinated animals, it will be possible to investigate the way in which different components of the innate and acquired immune system control the expansion( net growth rate) or determine the contraction( clearance) of the bacterial load in the tissues. Using combinations of defined bacterial mutants and gene-targeted mice, it will be possible to determine precisely how individual bacterial virulence genes counteract the antimicrobial functions of the host. Moreover, understanding the impact of the immune response and different classes of vaccines on the distribution and population dynamics of pathogens will be instrumental in furthering the development of preventive measures and drug therapies.
Global patterns and mechanistic determinants of bacterial spread in mammalian organisms are difficult to obtain through numerical and topographical mapping of a single bacterial population . Appreciation of the true pathogenetic events during infections needs to be based on the understanding of the fine interactions that control the infection dynamics of individual subpopulations in the same host . We have used molecular techniques to tag individually otherwise identical subpopulations of bacteria . We have used these bacteria , called wild-type isogenic tagged strains ( WITS ) , in simultaneous infections in the same animal to gather insights into the patterns of spread of individual subpopulations of bacteria in the tissues and interactions between bacteria and phagocytes . Combining numerical fluctuation in the WITS populations with mathematical modelling and statistical analysis , we have gathered data on the relative occurrence of bacterial growth and death in different phases of the disease process . Our analyses support a model in which shortly after infection , concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs . Later , decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally , with subsequent mixing of bacteria between organs . The work illustrates the importance of unravelling heterogeneous traits of infections to reconstruct and understand the true nature of the global disease process .