Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology

Abstract

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

Keywords: cytokines infectious disease; granuloma; immune response; mathematical modelling; mycobacterium tuberculosis.

FIGURE 1

Dynamics of pro- and anti-inflammatory immune responses steer disease progression along various trajectories. Schematic of relative pro- and anti-inflammatory responses to a pathogen over time corresponding to various host outcomes (A). Schematic of disease trajectories corresponding to qualitative ratios of pro- and anti-inflammatory responses (B). The outcomes are (i) high pathogen burden (blue); (ii) severe tissue damage (red); (iii) high pathogen burden along with a large amount of tissue damage (gray); (iv) a cleared infection and return to base levels of inflammation (white); (v) a dynamically balanced pro- and anti-inflammatory response to control pathogen growth and limit host pathology (purple)

FIGURE 2

Multi-organ events following infection with Mycobacterium tuberculosis. Mtb is inhaled into lungs and engulfed by macrophages where intracellular replication occurs. Additionally, DCs take up Mtb and traffic to lung-draining LNs via afferent lymphatics, where they prime T cells that have been recruited from high endothelial venules (HEV). These primed T cells migrate to lungs via efferent lymphatics and participate in granuloma formation and function by activating macrophages, secreting cytokines, and participating in adaptive immune responses

FIGURE 3

Computational and experimental visualizations of various granulomas. A zoomed in 2-D GranSim snapshot (A), which shows macrophage substates (green-resting, blue-activated, orange-infected, red-chronically infected), T-cell phenotypes (pink IFN-producing, purple-cytotoxic, light blue-Tregs), extracellular bacteria (yellow), necrosis (white). (B) is a GranSim snapshot of a different granuloma that shows the concentration gradient of TNF (units on colorbar are molecules of TNF per grid compartment). (C) is a macaque granuloma image stained for macrophages (Red CD68), T cells (Green CD3), and neutrophils (Blue calprotectin). (D) is a time plot of CFU/lesion NHP data (red, median with error bars indicating min/max) along with GranSim simulations with model predictions (median–solid line, min/max–dashed lines). Simulations plotted as in with a newer generation of GranSim and data derived from 32 NHP in previously published works ^,,,,

FIGURE 4

Balanced immune responses lead to contained Mtb granulomas. The top row (A, B, C) displays conceptual images of granuloma formation across a spectrum of immune responses. During a primarily anti-inflammatory response (A), a granuloma shows a lack of proper formation, and large amounts of caseum develop as the host struggles to contain the pathogen. A balanced response (B) displays proper granuloma formation and containment of the pathogen. A primarily pro-inflammatory response (C) clears the pathogen, but at the cost of widespread cellular activation, death, and inflammation. The bottom row (D, E, F) displays GranSim simulation snapshots of these scenarios at day 150 post infection. A containment simulation from baseline parameter ranges is demonstrated in (E), whereas a simulation from a TNF-depletion parameter set is shown in (D), and a simulation from an IL-10 knockout parameter set that also exhibits more pro-inflammatory behavior (F). (D), (E), and (F) correspond to the conceptual immune responses displayed in (A), (B), and (C), respectively

FIGURE 5

Cytokine distribution across in silico and bead model granulomas. (A) GranSim snapshot of a representative containment granuloma scenario at 50 days post infection. The TNF (red) and IL-10 (blue) gradients of this representative granuloma are shown in (B). In GranSim granulomas, the gradient of TNF is contained within the granuloma (see diameter of granuloma on x-axis), and the gradient of IL-10 spreads along the diameter of the granuloma. This particular set of gradients is representative of containment granulomas and is consistent across time. If the spatial range of TNF is too large, bystander cellular death increases despite little increase in bacterial killing. If TNF spatial distribution is too narrow, simulations show increased bacterial load. Similarly, if IL-10 has a spatial distribution that is too wide, IL-10 decreases bystander cell death, but causes increased bacterial loads in the granuloma. Yet, too narrow of an IL-10 distribution yields too powerful of a pro-inflammatory response. (C) The well-established PPD bead model of granuloma formation in mice (protocol in ^,–) can determine whether there is a gradient of soluble TNF within a single granuloma and detect soluble TNF gradients in ex vivo granuloma tissue. Immunofluorescence techniques identify the spatial organization of cells in granulomas, and then this known organization is combined with flow cytometry data, a simple receptor-ligand model, and fluorescent microscopy of unbound TNF receptors to reconstruct the relative soluble TNF gradient. A molecular probe from biotinylation of recombinant TNF was constructed to target unbound TNF receptors.^– In the representative PPD bead granuloma shown, stains are for free TNFR (yellow) using the biotinylated TNF probe and cell nuclei (blue) using DAPI. The dashed white circle indicates approximate PPD bead location. Entire image was used. (D) Intensity of biotinylated TNF in image from (C) as a function of radial distance from the center of the PPD bead, binned into 25 pixel lengths. Unbound TNFR is roughly inversely proportional to gradient of soluble TNF (derived from the simple receptor-ligand model). Bars indicate standard deviation

FIGURE 6

Simulated antibiotic distributions in granulomas using GranSim. Heat maps of isoniazid (INH) concentration for the 3 representative granulomas shown in Figure 4: one that is highly anti-inflammatory (A), one that is contained (B), and one that is highly pro-inflammatory (C). Each type of granuloma has lower concentrations of antibiotic compared to the surrounding tissue. The heat maps show INH concentration in granulomas after 150 days of infection and 2 hours after a single, oral dose of 5 mg/kg. PK/PD modeled as in^,

FIGURE 7

Effects of host-directed therapies on treatment outcome. (A) An in silico biorepository of 232 granulomas was created using GranSim and an infection period of 200 days. Starting at day 200, different treatments were simulated for 180 days: antibiotics alone (gray), antibiotics with varying doses of etanercept (500 μg/kg in red, 250 μg/kg in orange, 100 μg/kg in yellow), antibiotics with a 20% TNF-depletion (green), and antibiotics with 20% TNF-depletion and 10% IL-10 promotion (blue). Percent depletions and promotions were simulated by reducing or increasing the amount of cytokine secreted from immune cells compared to a baseline simulation. (B) The percent of granulomas that contain bacteria over the course of treatment shows that antibiotics alone sterilize more granulomas and sterilize faster than any of the host-directed therapies. (C) Host-directed therapies tend to have higher median CFU compared to antibiotics alone from 10-20 days post-treatment and later. (D) Simulations involving host-directed therapies result in granulomas that gradually decrease in size, compared to those involving only antibiotics (only medians shown)