Within-host dynamics of infection: from ecological insights to evolutionary predictions.

O.R. is supported by a University Research Fellowship from The Royal Society.This is the final version of the article. It first appeared from Royal Society Publishing via http://dx.doi.org/10.1098/rstb.2014.030

The evolution of sex-specific immune defences

Why do males and females often differ in their ability to cope with infection? Beyond physiological mechanisms, it has recently been proposed that life-history theory could explain immune differences from an adaptive point of view in relation to sex-specific reproductive strategies. However, a point often overlooked is that the benefits of immunity, and possibly the costs, depend not only on the host genotype but also on the presence and the phenotype of pathogens. To address this issue we developed an adaptive dynamic model that includes host–pathogen population dynamics and host sexual reproduction. Our model predicts that, although different reproductive strategies, following Bateman's principle, are not enough to select for different levels of immunity, males and females respond differently to further changes in the characteristics of either sex. For example, if males are more exposed to infection than females (e.g. for behavioural reasons), it is possible to see them evolve lower immunocompetence than females. This and other counterintuitive results highlight the importance of ecological feedbacks in the evolution of immune defences. While this study focuses on sex-specific natural selection, it could easily be extended to include sexual selection and thus help to understand the interplay between the two processes

Spread and transmission of bacterial pathogens in experimental populations of the nematode Caenorhabditis elegans.

Caenorhabditis elegans is frequently used as a model species for the study of bacterial virulence and innate immunity. In recent years, diverse mechanisms contributing to the nematode's immune response to bacterial infection have been discovered. Yet despite growing interest in the biochemical and molecular basis of nematode-bacterium associations, many questions remain about their ecology. Although recent studies have demonstrated that free-living nematodes could act as vectors of opportunistic pathogens in soil, the extent to which worms may contribute to the persistence and spread of these bacteria has not been quantified. We conducted a series of experiments to test whether colonization of and transmission between C. elegans nematodes could enable two opportunistic pathogens (Salmonella enterica and Pseudomonas aeruginosa) to spread on agar plates occupied by Escherichia coli. We monitored the transmission of S. enterica and P. aeruginosa from single infected nematodes to their progeny and measured bacterial loads both within worms and on the plates. In particular, we analyzed three factors affecting the dynamics of bacteria: (i) initial source of the bacteria, (ii) bacterial species, and (iii) feeding behavior of the host. Results demonstrate that worms increased the spread of bacteria through shedding and transmission. Furthermore, we found that despite P. aeruginosa's relatively high transmission rate among worms, its pathogenic effects reduced the overall number of worms colonized. This study opens new avenues to understand the role of nematodes in the epidemiology and evolution of pathogenic bacteria in the environment.Some C. elegans and bacteria strains were provided by the Caenorhabditis Genetics Centre, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). We thank Andrew Grant and Craig Winstanley for providing strains and reagents. We thank Mark Viney and two anonymous reviewers for helpful comments on the manuscript. This research was funded by a grant from the Biotechnology and Biological Sciences Research Council (grant number BB/I012222/1) to OR. OR also acknowledges funding from the Royal Society (University Research Fellowship).This is the author accepted manuscript. The final published version can be found on the publisher's website at: http://aem.asm.org/content/early/2014/06/23/AEM.01037-14.long Copyright © 2014, American Society for Microbiology. All Rights Reserve

Integrating mathematical models with experimental data to investigate the within-host dynamics of bacterial infections.

Bacterial infections still constitute a major cause of mortality and morbidity worldwide. The unavailability of therapeutics, antimicrobial resistance and the chronicity of infections due to incomplete clearance contribute to this phenomenon. Despite the progress in antimicrobial and vaccine development, knowledge about the effect that therapeutics have on the host-bacteria interactions remains incomplete. Insights into the characteristics of bacterial colonization and migration between tissues and the relationship between replication and host- or therapeutically induced killing can enable efficient design of treatment approaches. Recently, innovative experimental techniques have generated data enabling the qualitative characterization of aspects of bacterial dynamics. Here, we argue that mathematical modeling as an adjunct to experimental data can enrich the biological insight that these data provide. However, due to limited interdisciplinary training, efforts to combine the two remain limited. To promote this dialogue, we provide a categorization of modeling approaches highlighting their relationship to data generated by a range of experimental techniques in the area of in vivo bacterial dynamics. We outline common biological themes explored using mathematical models with case studies across all pathogen classes. Finally, this review advocates multidisciplinary integration to improve our mechanistic understanding of bacterial infections and guide the use of existing or new therapies

An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo.

The management of bacterial diseases calls for a detailed knowledge about the dynamic changes in host-bacteria interactions. Biological insights are gained by integrating experimental data with mechanistic mathematical models to infer experimentally unobservable quantities. This inter-disciplinary field would benefit from experiments with maximal information content yielding high-precision inference. Here, we present a computationally efficient tool for optimizing experimental design in terms of parameter inference in studies using isogenic-tagged strains. We study the effect of three experimental design factors: number of biological replicates, sampling timepoint selection and number of copies per tagged strain. We conduct a simulation study to establish the relationship between our optimality criterion and the size of parameter estimate confidence intervals, and showcase its application in a range of biological scenarios reflecting different dynamics patterns observed in experimental infections. We show that in low-variance systems with low killing and replication rates, predicting high-precision experimental designs is consistently achieved; higher replicate sizes and strategic timepoint selection yield more precise estimates. Finally, we address the question of resource allocation under constraints; given a fixed number of host animals and a constraint on total inoculum size per host, infections with fewer strains at higher copies per strain lead to higher-precision inference

Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface.

Funder: Alborada TrustThe Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife-human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host-pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread

Nested sampling for Bayesian model comparison in the context of Salmonella disease dynamics.

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered.RD was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (grant number BB/I002189/1). TJM was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (grant number BB/I012192/1). OR was funded by the Royal Society.This paper was originally published in PLOS ONE (Dybowski R, McKinley TJ, Mastroeni P, Restif O, PLoS ONE 2013, 8(12): e82317. doi:10.1371/journal.pone.0082317)

Within-host spatiotemporal dynamic of systemic salmonellosis: Ways to track infection, reaction to vaccination and antimicrobial treatment.

During the last two decades our understanding of the complex in vivo host-pathogen interactions has increased due to technical improvements and new research tools. The rapid advancement of molecular biology, flow cytometry and microscopy techniques, combined with mathematical modelling, have empowered in-depth studies of systemic bacterial infections across scales from single molecules, to cells, to organs and systems to reach the whole organism level. By tracking subpopulations of bacteria in vivo using molecular or fluorescent tags, it has been possible to reconstruct the spread of infection within and between organs, allowing unprecedented quantification of the effects of antimicrobial treatment and vaccination. This review illustrates recent advances in the study of heterogeneous traits of the infection process and illustrate approaches to investigate the reciprocal interactions between antimicrobial treatments, bacterial growth/death as well as inter- and intra-organ spread. We also discuss how vaccines impact the in vivo behaviour of bacteria and how these findings can guide vaccine design and rational antimicrobial treatment

Quantification of the effects of antibodies on the extra- and intracellular dynamics of Salmonella enterica.

Antibodies are known to be essential in controlling Salmonella infection, but their exact role remains elusive. We recently developed an in vitro model to investigate the relative efficiency of four different human immunoglobulin G (IgG) subclasses in modulating the interaction of the bacteria with human phagocytes. Our results indicated that different IgG subclasses affect the efficacy of Salmonella uptake by human phagocytes. In this study, we aim to quantify the effects of IgG on intracellular dynamics of infection by combining distributions of bacterial numbers per phagocyte observed by fluorescence microscopy with a mathematical model that simulates the in vitro dynamics. We then use maximum likelihood to estimate the model parameters and compare them across IgG subclasses. The analysis reveals heterogeneity in the division rates of the bacteria, strongly suggesting that a subpopulation of intracellular Salmonella, while visible under the microscope, is not dividing. Clear differences in the observed distributions among the four IgG subclasses are best explained by variations in phagocytosis and intracellular dynamics. We propose and compare potential factors affecting the replication and death of bacteria within phagocytes, and we discuss these results in the light of recent findings on dormancy of Salmonella.This work was funded by grants from the Wellcome Trust and from the Medical Research Council to PM. O.R. is supported by the Royal Society through a University Research Fellowship. M.P. is supported by a studentship from the Wellcome Trust

The demography of free-roaming dog populations and applications to disease and population control

Understanding the demography of domestic dog populations is essential for effective disease control, particularly of canine-mediated rabies. Demographic data are also needed to plan effective population management. However, no study has comprehensively evaluated the contribution of demographic processes (i.e. births, deaths and movement) to variations in dog population size or density, or determined the factors that regulate these processes, including human factors. We report the results of a 3-year cohort study of domestic dogs, which is the first to generate detailed data on the temporal variation of these demographic characteristics. The study was undertaken in two communities in each of Bali, Indonesia and Johannesburg, South Africa, in rabies-endemic areas and where the majority of dogs were free-roaming. None of the four communities had been engaged in any dog population management interventions by local authorities or animal welfare organizations. All identified dogs in the four communities were monitored individually throughout the study. We observed either no population growth or a progressive decline in population size during the study period. There was no clear evidence that population size was regulated through environmental resource constraints. Rather, almost all of the identified dogs were owned and fed regularly by their owners, consistent with population size regulated by human demand. Finally, a substantial fraction of the dogs originated from outside the population, entirely through the translocation of dogs by people, rather than from local births. These findings demonstrate that previously reported growth of dog populations is not a general phenomenon and challenge the widely held view that free-roaming dogs are unowned and form closed populations. Synthesis and applications. These observations have broad implications for disease and population control. The accessibility of dogs for vaccination and evaluation through owners and the movement of dogs (some of them infected) by people will determine the viable options for disease control strategies. The impact of human factors on population dynamics will also influence the feasibility of annual vaccination campaigns to control rabies and population control through culling or sterilization. The complex relationship between dogs and people is critically important in the transmission and control of canine-mediated rabies. For effective management, human factors must be considered in the development of disease and population control programmes