An investigation of tuberculosis progression revealing the role of macrophages apoptosis via sensitivity and bifurcation analysis

Type I interferon exacerbates Mycobacterium tuberculosis induced human macrophage death

Type I interferons (IFN-I) are implicated in exacerbation of tuberculosis (TB), but the mechanisms are unclear. Mouse macrophages infected with Mycobacterium tuberculosis (Mtb) produce IFN-I, which contributes to their death. Here we investigate whether the same is true for human monocyte-derived macrophages (MDM). MDM prepared by a conventional method markedly upregulate interferon-stimulated genes (ISGs) upon Mtb infection, while MDM prepared to better restrict Mtb do so much less. A mixture of antibodies inhibiting IFN-I signaling prevents ISG induction. Surprisingly, secreted IFN-I are undetectable until nearly two days after ISG induction. These same antibodies do not diminish Mtb-infected MDM death. MDM induce ISGs in response to picogram/mL levels of exogenous IFN-I while depleting similar quantities from the medium. Exogenous IFN-I increase the proportion of dead MDM. We speculate that Mtb-infected MDM produce and respond to minute levels of IFN-I, and that only some of the resultant signaling is susceptible to neutralizing antibodies. Many types of cells may secrete IFN-I in patients with TB, where IFN-I is likely to promote the death of infected macrophages.

Exploring Hopf-bifurcations and endemic bubbles in a tuberculosis model with behavioral changes and treatment saturation

To manage risks and minimize the transmission of contagious diseases, individuals may reduce their contact with each other and take other precautions as much as possible in their daily lives and workplaces. As a result, the transmission of the infection reduces due to the behavioral changes. These behavioral changes are incorporated into models by introducing saturation in disease incidence. In this article, we propose and analyze a tuberculosis model that incorporates saturated exogenous reinfection and treatment. The stability analysis of the model's steady states is rigorously examined. We observe that the disease-free equilibrium point and the endemic equilibrium point (EEP) are globally asymptotically stable if the basic reproduction number (R0) is less than 1 and greater than 1, respectively, only when exogenous reinfection is not present (p=0) and when treatment is available for all (ω=0). However, even when R0 is less than 1, tuberculosis may persist at a specific level in the presence of exogenous reinfection and treatment saturation, leading to a backward bifurcation in the system. The existence and direction of Hopf-bifurcations are also discussed. Furthermore, we numerically validate our analytical results using different parameter sets. In the numerical examples, we study Hopf-bifurcations for parameters such as β, p, α, and ω. In one example, we observe that increasing β leads to the loss of stability of the unique EEP through a forward Hopf-bifurcation. If β is further increased, the unique EEP restores its stability, and the bifurcation diagram exhibits an interesting structure known as an endemic bubble. The existence of an endemic bubble for the saturation constant ω is also observed.

Deterministic and stochastic in-host tuberculosis models for bacterium-directed and host-directed therapy combination

Mycobacterium tuberculosis (TB) infection can involve all immune system components and can result in different disease outcomes. The antibiotic TB drugs require strict adherence to prevent both disease relapse and mutation of drug- and multidrug-resistant strains. To overcome the constraints of pathogen-directed therapy, host-directed therapy has attracted more attention in recent years as an adjunct therapy to enhance host immunity to fight against this intractable pathogen. The goal of this paper is to investigate in-host TB models to provide insights into therapy development. Focusing on therapy-targeting parameters, the parameter regions for different disease outcomes are identified from an established ODE model. Interestingly, the ODE model also demonstrates that the immune responses can both benefit and impede disease progression, depending on the number of bacteria engulfed and released by macrophages. We then develop two Itô SDE models, which consider the impact of demographic variations at the cellular level and environmental variations during therapies along with demographic variations. The SDE model with demographic variation suggests that stochastic fluctuations at the cellular level have significant influences on (1) the T-cell population in all parameter regions, (2) the bacterial population when parameters located in the region with multiple disease outcomes and (3) the uninfected macrophage population in the parameter region representing active disease. Further, considering environmental variations from therapies, the second SDE model suggests that disease progression can slow down if therapies (1) can have fast return rates and (2) can bring parameter values into the disease clearance regions.