A Model to Predict Cell-Mediated Immune Regulatory Mechanisms During Human Infection withMycobacterium tuberculosis

Abstract

A key issue for the study of tuberculosis infection (TB) is to understand why individuals infected with Mycobacterium tuberculosis experience different clinical outcomes. Elaborating the immune mechanisms that determine whether an infected individual will suffer active TB or latent infection can aid in developing treatment and prevention strategies. To better understand the dynamics of M. tuberculosis infection and immunity, we have developed a virtual human model that qualitatively and quantitatively characterizes the cellular and cytokine control network operational during TB infection. Using this model, we identify key regulatory elements in the host response. In particular, factors affecting cell functions, such as macrophage activation and bactericidal capabilities, and effector T cell functions such as cytotoxicity and cytokine production can each be determinative. The model indicates, however, that even if latency is achieved, it may come at the expense of tissue damage if the response is not properly regulated. A balance in Th1 and Th2 immune responses governed by IFN-γ, IL-10, and IL-4 facilitate this down-regulation. These results are further explored through virtual deletion and depletion experiments.

The Effects of HIV-1 Infection on Latent Tuberculosis

“…Each infected macrophage carries approximately 10 bacteria (computed from number of infected macrophages divided by the number of intracellular bacteria in steady state). Cellular and cytokine level dynamics agree with those obtained previously [58][55] [37]. Our simulations show that Mtb infection can also result in active TB disease (Fig.…”

“…On the other hand, interleukin-10 (IL-10) production by macrophages and T-cells during the course of Mtb infection deactivates macrophages [21], inhibits CD4 + T-cell responses [16], and down regulates IL-12 production [16], which consequently decreases IFN-γ production. The importance of these negative regulators of immunity have only recently been elucidated [58]. One can easily see the complexities and interconnectivity of the immune response to M. tuberculosis and how an alteration in the competency of the immune system can disrupt its delicate balance (for better or for worse).…”

Modeling the immune rheostat of macrophages in the lung in response to infection

“…B i are either extracellular bacteria that were directly phagocytosed by A, becoming intracellular bacteria (13,35) or the result of bacterial growth of B i in A i . It is assumed that B i grow in A i according to a quadratic logistic growth (12,21,27,35). In addition, through exposure to I g and I a , A i can become activated to inhibit the growth of B i (39).…”

“…The present article builds on some of this work and on other models of tuberculosis (19)(20)(21). However, these former models of tuberculosis infection do not specifically consider AAM and instead focus on the paradigm involving only macrophage populations (resting, activated, and infected) that are activated/primed by IFN-␥/TNF-␣ (i.e., CAM) Although it is clear that during the course of an MTb infection CAM are involved, the present work focuses on the fact that AAM are the first to encounter and respond to MTb.…”

Математическая модель гуморального иммунного ответа для исследования Th17 аутоиммунитета

“…Estimation of cytokine concentration and dynamics in situ and interpretation of such data is a very difficult problem, because the level of cytokines in the place, where intensive dynamic processes occur, may vary by orders in a short time, and the number of cytokine-synthesizing cells is also not always possible to determine precisely. Nevertheless, on the basis of several works one can roughly approximate the rate of cytokines synthesis and its dynamics in time [6,[13][14][15][16].…”