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

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.

Interpreting and de-noising genetically engineered barcodes in a DNA virus

The concept of a nucleic acid barcode applied to pathogen genomes is easy to grasp and the many possible uses are straightforward. But implementation may not be easy, especially when growing through multiple generations or assaying the pathogen long-term. The potential problems include: the barcode might alter fitness, the barcode may accumulate mutations, and construction of the marked pathogens may result in unintended barcodes that are not as designed. Here, we generate approximately 5,000 randomized barcodes in the genome of the prototypic small DNA virus murine polyomavirus. We describe the challenges faced with interpreting the barcode sequences obtained from the library. Our Illumina NextSeq sequencing recalled much greater variation in barcode sequencing reads than the expected 5,000 barcodes-necessarily stemming from the Illumina library processing and sequencing error. Using data from defined control virus genomes cloned into plasmid backbones we develop a vetted post-sequencing method to cluster the erroneous reads around the true virus genome barcodes. These findings may foreshadow problems with randomized barcodes in other microbial systems and provide a useful approach for future work utilizing nucleic acid barcoded pathogens.

Synthesis of a Two-Dimensional Molybdenum Disulfide Nanosheet and Ultrasensitive Trapping of Staphylococcus Aureus for Enhanced Photothermal and Antibacterial Wound-Healing Therapy

Photothermal therapy has been widely used in the treatment of bacterial infections. However, the short photothermal effective radius of conventional nano-photothermal agents makes it difficult to achieve effective photothermal antibacterial activity. Therefore, improving composite targeting can significantly inhibit bacterial growth. We inhibited the growth of Staphylococcus aureus (S. aureus) by using an extremely low concentration of vancomycin (Van) and applied photothermal therapy with molybdenum disulfide (MoS₂). This simple method used chitosan (CS) to synthesize fluorescein 5(6)-isothiocyanate (FITC)-labeled and Van-loaded MoS₂-nanosheet hydrogels (MoS₂-Van-FITC@CS). After modifying the surface, an extremely low concentration of Van could inhibit bacterial growth by trapping bacteria synergistically with the photothermal effects of MoS₂, while FITC labeled bacteria and chitosan hydrogels promoted wound healing. The results showed that MoS₂-Van-FITC@CS nanosheets had a thickness of approximately 30 nm, indicating the successful synthesis of the nanosheets. The vitro antibacterial results showed that MoS₂-Van-FITC with near-infrared irradiation significantly inhibited S. aureus growth, reaching an inhibition rate of 94.5% at nanoparticle concentrations of up to 100 µg/mL. Furthermore, MoS₂-Van-FITC@CS could exert a healing effect on wounds in mice. Our results demonstrate that MoS₂-Van-FITC@CS is biocompatible and can be used as a wound-healing agent.

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice

Simple Summary

Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.

Abstract

The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

RESTAMP - Rate estimates by sequence-tag analysis of microbial populations

Microbial division rates determine the speed of mutation accumulation and thus the emergence of antimicrobial resistance. Microbial death rates are affected by antibiotic action and the immune system. Therefore, measuring these rates has advanced our understanding of host-pathogen interactions and antibiotic action. Several methods based on marker-loss or few inheritable neutral markers exist that allow estimating microbial division and death rates, each of which has advantages and limitations. Technical bottlenecks, i.e., experimental sampling events, during the experiment can distort the rate estimates and are typically unaccounted for or require additional calibration experiments. In this work, we introduce RESTAMP (Rate Estimates by Sequence Tag Analysis of Microbial Populations) as a method for determining bacterial division and death rates. This method uses hundreds of fitness neutral sequence barcodes to measure the rates and account for experimental bottlenecks at the same time. We experimentally validate RESTAMP and compare it to established plasmid loss methods. We find that RESTAMP has a number of advantages over plasmid loss or previous marker based techniques. (i) It enables to correct the distortion of rate estimates by technical bottlenecks. (ii) Rate estimates are independent of the sequence tag distribution in the starting culture allowing the use of an arbitrary number of tags. (iii) It introduces a bottleneck sensitivity measure that can be used to maximize the accuracy of the experiment. RESTAMP allows studying microbial population dynamics with great resolution over a wide dynamic range and can thus advance our understanding of host-pathogen interactions or the mechanisms of antibiotic action.

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.

Modelling of enzyme kinetics: cellulose enzymatic hydrolysis case

Enzymes as industrial biocatalysts offer numerous advantages over traditional chemical processes resulting on improvements in process economy and environmental sustainability. Because enzymes are extensively used in different industrial areas, the enzyme kinetics is an important factor for industry as it is able to estimate the extent of substrate conversion under known conditions and evaluate reactor performance. Furthermore, kinetic modelling is useful in the analysis, prediction, and optimization of an enzymatic process. Thus, kinetic modelling is a powerful tool for biochemical reaction engineering. In addition to the aforementioned, in the industrial technology, modelling together with simulation play a key role because they help to understand how a system behaves under specific conditions, and thus they allow saving on costs and lead times. Enzymatic conversion of renewable cellulosic biomass into biofuels is at the heart of advanced bioethanol production. In the production of bioethanol from cellulosic biomass, enzymatic hydrolysis of cellulose to fermentable sugars accounts for a large portion (∼30%) of the total production costs. Therefore, a thorough understanding of enzymatic hydrolysis is necessary to create a robust model which helps designing optimal conditions and economical system. Nevertheless, it is a challenging task because cellulose is a highly complex substrate and its enzymatic hydrolysis is heterogeneous in nature, and thus the whole process of cellulose conversion to glucose involves more steps than classical enzyme kinetics. This chapter describes the bases of enzyme kinetic modelling, focussing on Michaelis-Menten kinetics, and presents the models classification based on the fundamental approach and methodology used. Furthermore, the modelling of cellulose enzymatic hydrolysis is described, also reviewing some model examples developed for cellulose hydrolysis over the years. Finally, the application of enzyme kinetics modelling in food, pharmaceutical and bioethanol industry is presented.

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.