A Bioluminescent Francisella tularensis SCHU S4 Strain Enables Noninvasive Tracking of Bacterial Dissemination and the Evaluation of Antibiotics in an Inhalational Mouse Model of Tularemia

Multi-bioluminescence based dynamic imaging of Pseudomonas aeruginosa-induced hepatic inflammation process

Bacterial infections are a major cause of death worldwide. However, it is difficult to track the in vivo dynamics of pathogenic bacteria and the expression of inflammatory factors in infected animals throughout the infection process. This work used Pseudomonas aeruginosa as an infection model and utilised genetically bioluminescence-labeled P. aeruginosa and hydrodynamic transfection technology to construct a liver-visual NF-κB, IL-6, TNF-α inflammation model, thereby enabling the tracking of the dynamic spread of P. aeruginosa in infected animals and the transient activation of the liver inflammation response. The results showed that P. aeruginosa introduced via the tail vein initially accumulates in the liver and gradually activates NF-κB, IL-6, and TNF-α. Subsequently, the P. aeruginosa infection gradually spreads to the lungs and small intestine, and final proliferation leads to septic death in mice. During the infection process, we observed a strictly negative correlation between platelet activation and bacterial proliferation; the higher the degree of platelet activation, the stronger the inhibitory effect on bacterial proliferation and liver inflammation. In conclusion, this bioluminescence-based in vivo imaging technique offers new opportunities to investigate the innate immune response in controlling pathogenic infections.

Advances in the Development of Bacterial Bioluminescence Imaging

Bioluminescence imaging (BLI) is a powerful method for visualizing biological processes and tracking cells. Engineered bioluminescent bacteria that utilize luciferase-catalyzed biochemical reactions to generate luminescence have become useful analytical tools for in vitro and in vivo bacterial imaging. Accordingly, this review initially introduces the development of engineered bioluminescent bacteria that use different luciferase-luciferin pairs as analytical tools and their applications for in vivo BLI, including real-time bacterial tracking of infection, probiotic investigation, tumor-targeted therapy, and drug screening. Applications of engineered bioluminescent bacteria as whole-cell biosensors for sensing biological changes in vitro and in vivo are then discussed. Finally, we review the optimizations and future directions of bioluminescent bacteria for imaging. This review aims to provide fundamental insights into bacterial BLI and highlight the potential development of this technique in the future.

A lux-based Staphylococcus aureus bioluminescence screening assay for the detection/identification of antibiotics and prediction of antibiotic mechanisms

The need for the discovery of new antibiotics and solving the antibiotic resistance problem requires rapid detection of antibiotics, identification of known antibiotics, and prediction of antibiotic mechanisms. The bacterial lux genes encode proteins that convert chemical energy into photonic energy and lead to bioluminescence. Exploiting this phenomenon, we constructed a lux-based bioluminescence system in Staphylococcus aureus by expressing lux genes under the control of stress-inducible chaperon promoters. When experiencing antibiotic stress, these constructed reporter strains showed clear bioluminescence response. Therefore, this bioluminescence screening system can be used for the detection of antibiotics in unknown chemical mixtures. Further analysis of bioluminescence response patterns showed that: (1) these bioluminescence response patterns are highly antibiotic specific and therefore can be used for rapid and cheap identification of antibiotics; and that (2) antibiotics having the same mechanism of action have similar bioluminescence patterns and therefore these patterns can be used for the prediction of mechanism for an unknown antibiotic with good sensitivity and specificity. With this bioluminescence screening assay, the discovery and analysis of new antibiotics can be promoted, which benefits in solving the antibiotic resistance problem.

Stochastic dynamics of Francisella tularensis infection and replication

We study the pathogenesis of Francisella tularensis infection with an experimental mouse model, agent-based computation and mathematical analysis. Following inhalational exposure to Francisella tularensis SCHU S4, a small initial number of bacteria enter lung host cells and proliferate inside them, eventually destroying the host cell and releasing numerous copies that infect other cells. Our analysis of disease progression is based on a stochastic model of a population of infectious agents inside one host cell, extending the birth-and-death process by the occurrence of catastrophes: cell rupture events that affect all bacteria in a cell simultaneously. Closed expressions are obtained for the survival function of an infected cell, the number of bacteria released as a function of time after infection, and the total bacterial load. We compare our mathematical analysis with the results of agent-based computation and, making use of approximate Bayesian statistical inference, with experimental measurements carried out after murine aerosol infection with the virulent SCHU S4 strain of the bacterium Francisella tularensis, that infects alveolar macrophages. The posterior distribution of the rate of replication of intracellular bacteria is consistent with the estimate that the time between rounds of bacterial division is less than 6 hours in vivo.

Infecting a host cell is required for the replication of many types of bacteria and viruses. In some cases, infected cells release new infectious agents continuously over their lifetime. In others, such as the Francisella tularensis bacterium studied here, they are released in a single burst that coincides with the cell’s death. We show how a stochastic model, the birth-and-death process with catastrophe, can be used to characterise infection in a single cell, thereby allowing us to account for burst events and quantify the kinetics of pathogenesis in the lung, the initial site of infection, as well as in other organs that the infection spreads to. We learn about the parameters of the mathematical model of Francisella tularensis infection making use of the experimental measurements of bacterial loads, together with approximate Bayesian statistical inference methods. The most important parameter describing the pathogenesis is the rate of replication of intracellular bacteria.