The timing of TNF and IFN-γ signaling affects macrophage activation strategies during Mycobacterium tuberculosis infection

Abstract

During most infections, the population of immune cells known as macrophages are key to taking up and killing bacteria as an integral part of the immune response. However, during infection with Mycobacterium tuberculosis (Mtb), host macrophages serve as the preferred environment for mycobacterial growth. Further, killing of Mtb by macrophages is impaired unless they become activated. Activation is induced by stimulation from bacterial antigens and inflammatory cytokines derived from helper T cells. The key macrophage-activating cytokines in Mtb infection are tumor necrosis factor-α (TNF) and interferon (IFN)-γ. Due to differences in cellular sources and secretion pathways for TNF and IFN-γ, the possibility of heterogeneous cytokine distributions exists, suggesting that the timing of macrophage activation from these signals may affect activation kinetics and thus impact the outcome of Mtb infection. Here we use a mathematical model to show that negative feedback from production of nitric oxide (the key mediator of mycobacterial killing) that typically optimizes macrophage responses to activating stimuli may reduce effective killing of Mtb. Statistical sensitivity analysis predicts that if TNF and IFN-γ signals precede infection, the level of negative feedback may have a strong effect on how effectively macrophages kill Mtb. However, this effect is relaxed when IFN-γ or TNF + IFN-γ signals are received coincident with infection. Under these conditions, the model suggests that negative feedback induces fast responses and an initial overshoot of nitric oxide production for given doses of TNF and IFN-γ, favoring killing of Mtb. Together, our results suggest that direct entry of macrophages into a granuloma site (and not distal to it) from lung vascular sources represents a preferred host strategy for mycobacterial control. We examine implications of these results in establishment of latent Mtb infection.

Computational modeling of cytokine signaling in microglia

“…56 Similar adaptations of the S-systems model haven been useful in previous models involving cytokine signaling. 57,58,95,96 A key assumption of our model was that the integrated effects of input cytokines on their target are governed by AND gating. While we do not have specific evidence validating this assumption for all connections in the network, both computational and experimental data indicate that AND gating is common in intracellular signaling networks involved in coordinating cytokine responses and production.…”

Computational modeling of tuberculous meningitis reveals an important role for tumor necrosis

“…Initially, microglia are in the ramified state. In addition to the states, we consider the two transcription factors NF κ B and STAT1 [45]. …”

Mathematical model of mycobacterium–host interaction describes physiology of persistence

“…77 • The growth of mycobacteria was modelled through a logistic equation with a fixed growth 78 rate as assumed by previous work (Ray JCJ, 2008). 79 • The model we proposed contains a minimalist description of lipid metabolism and iron 80 trafficking and does not reach the level of molecular level detail that exists in the biological 81 systems.…”

“…Despite the functional and 86 metabolic heterogeneity of lipid molecules, the overall lipid content was accounted within 87 the same variable. 88 • To simplify the innate immunity scenario, we included in our model only Nitric Oxide (N), 89 which was considered as the sole effective weapon against infection growth followed a logistic curve with a fixed growth rate (α 1 ), whose value was taken from 97 published work (Vijay S, 2014;Gil W, 2009;Ray JCJ, 2008). During early stages of infection, 98 which naturally occur at a low dose (Saini D, 2012), bacilli are confined in the infected 99 macrophages.…”

“…Our model described a population of cells that accounted for both infected and 100 uninfected macrophages. Thus, to be representative of the average level of infection in the 101 entire population, the carrying capacity (β 1 ) (Ray JCJ, 2008) was adjusted to 5. Iron enhances 102 the growth of bacteria, this effect was modelled with (λ 1 Fe) and the microbicidal effect due to 103 N appeared as (-λ 3 N).…”