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

Quantification of Salmonella enterica serovar Typhimurium Population Dynamics in Murine Infection Using a Highly Diverse Barcoded Library

Murine models are often used to study the pathogenicity and dissemination of the enteric pathogen Salmonella enterica serovar Typhimurium. Here, we quantified S. Typhimurium population dynamics in mice using the STAMPR analytic pipeline and a highly diverse S. Typhimurium barcoded library containing ~55,000 unique strains distinguishable by genomic barcodes by enumerating S. Typhimurium founding populations and deciphering routes of spread in mice. We found that a severe bottleneck allowed only one in a million cells from an oral inoculum to establish a niche in the intestine. Furthermore, we observed compartmentalization of pathogen populations throughout the intestine, with few barcodes shared between intestinal segments and feces. This severe bottleneck widened and compartmentalization was reduced after streptomycin treatment, suggesting the microbiota plays a key role in restricting the pathogen's colonization and movement within the intestine. Additionally, there was minimal sharing between the intestine and extraintestinal organ populations, indicating dissemination to extraintestinal sites occurs rapidly, before substantial pathogen expansion in the intestine. Bypassing the intestinal bottleneck by inoculating mice via intravenous or intraperitoneal injection revealed that Salmonella re-enters the intestine after establishing niches in extraintestinal sites by at least two distinct pathways. One pathway results in a diverse intestinal population. The other re-seeding pathway is through the bile, where the pathogen is often clonal, leading to clonal intestinal populations and correlates with gallbladder pathology. Together, these findings deepen our understanding of Salmonella population dynamics.

How colonization bottlenecks, tissue niches, and transmission strategies shape protozoan infections

Protozoan pathogens such as Plasmodium spp., Leishmania spp., Toxoplasma gondii, and Trypanosoma spp. are often associated with high-mortality, acute and chronic diseases of global health concern. For transmission and immune evasion, protozoans have evolved diverse strategies to interact with a range of host tissue environments. These interactions are linked to disease pathology, yet our understanding of the association between parasite colonization and host homeostatic disruption is limited. Recently developed techniques for cellular barcoding have the potential to uncover the biology regulating parasite transmission, dissemination, and the stability of infection. Understanding bottlenecks to infection and the in vivo tissue niches that facilitate chronic infection and spread has the potential to reveal new aspects of parasite biology.

Fitness advantage of Bacteroides thetaiotaomicron capsular polysaccharide in the mouse gut depends on the resident microbiota

Many microbiota-based therapeutics rely on our ability to introduce a microbe of choice into an already-colonized intestine. In this study, we used genetically barcoded Bacteroides thetaiotaomicron (B. theta) strains to quantify population bottlenecks experienced by a B. theta population during colonization of the mouse gut. As expected, this reveals an inverse relationship between microbiota complexity and the probability that an individual wildtype B. theta clone will colonize the gut. The polysaccharide capsule of B. theta is important for resistance against attacks from other bacteria, phage, and the host immune system, and correspondingly acapsular B. theta loses in competitive colonization against the wildtype strain. Surprisingly, the acapsular strain did not show a colonization defect in mice with a low-complexity microbiota, as we found that acapsular strains have an indistinguishable colonization probability to the wildtype strain on single-strain colonization. This discrepancy could be resolved by tracking in vivo growth dynamics of both strains: acapsular B.theta shows a longer lag phase in the gut lumen as well as a slightly slower net growth rate. Therefore, as long as there is no niche competitor for the acapsular strain, this has only a small influence on colonization probability. However, the presence of a strong niche competitor (i.e., wildtype B. theta, SPF microbiota) rapidly excludes the acapsular strain during competitive colonization. Correspondingly, the acapsular strain shows a similarly low colonization probability in the context of a co-colonization with the wildtype strain or a complete microbiota. In summary, neutral tagging and detailed analysis of bacterial growth kinetics can therefore quantify the mechanisms of colonization resistance in differently-colonized animals.

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.

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.

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 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.

Inferring within-host bottleneck size: A Bayesian approach

Recent technical developments in microbiology have led to new discoveries on the within-host dynamics of bacterial infections in laboratory animals. In particular, they have highlighted the importance of stochastic bottlenecks at the onset of invasive disease. A number of approaches exist for bottleneck-size estimation with respect to within-host bacterial infections; however, some are more appropriate than others under certain circumstances. A Bayesian comparison of several approaches is made in terms of the availability of isogenic multitype bacteria (e.g., WITS), knowledge of post-bottleneck dynamics, and the suitability of dilution with monotype bacteria. A sampling approach to bottleneck-size estimation is also introduced. The results are summarised by a guiding flowchart, which we hope will promote the use of quantitative models in microbiology to refine the analysis of animal experiment data.

Transcriptome and proteome analysis of Salmonella enterica serovar Typhimurium systemic infection of wild type and immune-deficient mice

Salmonella enterica are a threat to public health. Current vaccines are not fully effective. The ability to grow in infected tissues within phagocytes is required for S. enterica virulence in systemic disease. As the infection progresses the bacteria are exposed to a complex host immune response. Consequently, in order to continue growing in the tissues, S. enterica requires the coordinated regulation of fitness genes. Bacterial gene regulation has so far been investigated largely using exposure to artificial environmental conditions or to in vitro cultured cells, and little information is available on how S. enterica adapts in vivo to sustain cell division and survival. We have studied the transcriptome, proteome and metabolic flux of Salmonella, and the transcriptome of the host during infection of wild type C57BL/6 and immune-deficient gp91-/-phox mice. Our analyses advance the understanding of how S. enterica and the host behaves during infection to a more sophisticated level than has previously been reported.