Single passage in mouse organs enhances the survival and spread of Salmonella enterica.

Intravenous inoculation of Salmonella enterica serovar Typhimurium into mice is a prime experimental model of invasive salmonellosis. The use of wild-type isogenic tagged strains (WITS) in this system has revealed that bacteria undergo independent bottlenecks in the liver and spleen before establishing a systemic infection. We recently showed that those bacteria that survived the bottleneck exhibited enhanced growth when transferred to naive mice. In this study, we set out to disentangle the components of this in vivo adaptation by inoculating mice with WITS grown either in vitro or in vivo. We developed an original method to estimate the replication and killing rates of bacteria from experimental data, which involved solving the probability-generating function of a non-homogeneous birth-death-immigration process. This revealed a low initial mortality in bacteria obtained from a donor animal. Next, an analysis of WITS distributions in the livers and spleens of recipient animals indicated that in vivo-passaged bacteria started spreading between organs earlier than in vitro-grown bacteria. These results further our understanding of the influence of passage in a host on the fitness and virulence of Salmonella enterica and represent an advance in the power of investigation on the patterns and mechanisms of host-pathogen interactions.This work was funded by a Medical Research Council (MRC) grant (G0801161) awarded to AJG, PM and DJM. RD was supported by BBSRC grant BB/I002189/1 awarded to PM. OR is supported by a University Research Fellowship from the Royal Society.This is the final version of the article. It was first available from Royal Society Publishing via http://dx.doi.org/10.1098/rsif.2015.070

An efficient moments-based inference method for within-host bacterial infection dynamics.

Over the last ten years, isogenic tagging (IT) has revolutionised the study of bacterial infection dynamics in laboratory animal models. However, quantitative analysis of IT data has been hindered by the piecemeal development of relevant statistical models. The most promising approach relies on stochastic Markovian models of bacterial population dynamics within and among organs. Here we present an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT data. A common approach to statistical inference with stochastic dynamic models relies on producing large numbers of simulations, but this remains a slow and inefficient method for all but simple problems, especially when tracking bacteria in multiple locations simultaneously. Instead, we derive and solve the systems of ordinary differential equations for the two lower-order moments of the stochastic variables (mean, variance and covariance). For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. While both sets of parameter estimates had overlapping confidence regions, the new method produced lower values for the division and death rates of bacteria: these improved the goodness-of-fit at the second time point at the expense of that of the first time point. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design.Biotechnology and Biological Sciences Research Council grant BB/M020193/1 awarded to OR, and to support DJP. Biotechnology and Biological Sciences Research Council grant BB/I002189/1 awarded to PM, and to support RD

A Restricted Role for FcγR in the Regulation of Adaptive Immunity.

By their interaction with IgG immune complexes, FcγR and complement link innate and adaptive immunity, showing functional redundancy. In complement-deficient mice, IgG downstream effector functions are often impaired, as well as adaptive immunity. Based on a variety of model systems using FcγR-knockout mice, it has been concluded that FcγRs are also key regulators of innate and adaptive immunity; however, several of the model systems underpinning these conclusions suffer from flawed experimental design. To address this issue, we generated a novel mouse model deficient for all FcγRs (FcγRI/II/III/IV-/- mice). These mice displayed normal development and lymphoid and myeloid ontogeny. Although IgG effector pathways were impaired, adaptive immune responses to a variety of challenges, including bacterial infection and IgG immune complexes, were not. Like FcγRIIb-deficient mice, FcγRI/II/III/IV-/- mice developed higher Ab titers but no autoantibodies. These observations indicate a redundant role for activating FcγRs in the modulation of the adaptive immune response in vivo. We conclude that FcγRs are downstream IgG effector molecules with a restricted role in the ontogeny and maintenance of the immune system, as well as the regulation of adaptive immunity

The average mean absolute relative error (MARE) across a number of observation times, for the three-compartment network with migration in both directions between two compartments.

<p>Each point is the average for that measure, at that observation time, with error bars representing the standard error.</p

An efficient moments-based inference method for within-host bacterial infection dynamics - Fig 13

<p>Left: Diagram of each model illustrating the relevant compartments, initial conditions, and rates of interest. Right: MDE parameter estimate (blue dot) with box plots of the bootstrapped parameter estimates, and MLE parameter estimate (red cross) with 95% confidence interval (red bars).</p

Figures demonstrating the goodness-of-fit of the two models to the respective data sets.

<p>The histogram bars are the bootstrapped estimate of the null distribution of divergences under the model at the estimated parameter values for the respective model. The (blue) vertical dashed-line is the divergence corresponding to the observed data set.</p

Comparison of computation times to obtain MDE and MLE from simulated data.

<p>Each box plot comprises of 2430 runs of each estimation procedure: 30 simulated datasets (of size either 24, 40 or 80) were generated from each of 27 combinations of parameter values (see main text for complete list of combinations). The size of the experiment does not impact the computation time for either method.</p

Observed experimental data for both naive and vaccinated groups, at both observation times, compared to simulations from the model at parameters estimated by both the MLE and MDE methods, in each of the blood (B), liver (L) and spleen (S).

<p>Numbers of bacteria are the sums of eight identical WITS.</p