Mycobacterium tuberculosis is one of the world’s most deadly human pathogens; an integrated understanding of how it successfully survives in its host is crucial to developing new treatment strategies. One notable characteristic of infection with M. tuberculosis is the formation of granulomas, aggregates of immune cells whose structure and function may reflect success or failure of the host to contain infection. One central regulator of host responses to infection, including granuloma formation, is the pleiotropic cytokine TNF-α. Experimental work has characterized roles for TNF in macrophage activation; regulation of apoptosis; chemokine and cytokine production; and regulation of cellular recruitment via transendothelial migration. Separating the effects of these functions is presently difficult or impossible in vivo. To this end, we applied a computational model to understand specific roles of TNF in control of tuberculosis in a single granuloma. In the model, cells are represented as discrete entities on a spatial grid responding to environmental stimuli by following programmed rules determined from published experimental studies. Simulated granulomas emerge as a result of these rules. After confirming the importance of TNF in this model, we assessed the effects of individual TNF functions. The model predicts that multiple TNF activities contribute to control of infection within the granuloma, with macrophage activation as a key effector mechanism for controlling bacterial growth. Results suggest that bacterial numbers are a strong contributing factor to granuloma structure with TNF. Finally, TNF-dependent apoptosis may reduce inflammation at the cost of impairing mycobacterial clearance.
Tuberculosis (TB)4 kills more people per year than any other single infectious disease. Infection by its causative agent, Mycobacterium tuberculosis (Mtb), results in active disease in only a minority of cases (∼10%); the majority of infections are controlled and clinically silent, although the host often remains infected (reviewed in Ref. 1).
The classic feature of pulmonary Mtb infection arises as a result of the immune response, in which aggregates of immune cells and bacteria, called granulomas, form in the lungs. In humans and nonhuman primates with latent pulmonary infection, granulomas form as well-circumscribed masses in the lung parenchyma comprised of resting, infected, and activated macrophages with a characteristic cuff of T cells on the periphery (e.g., Refs. 2 and 3) and a caseous necrotic center (4). Macrophages within a granuloma have dual roles in Mtb infection: they are the primary mechanism for Mtb containment and the preferred location for bacterial growth. During infection, there are often multiple and different types of granulomas within the lung; each could have a different outcome depending on local environment. At the level of a single granuloma, macrophages may fail to control infection, leading to necrotic granulomas harboring large numbers of bacteria within macrophages (3). However, the relationship between bacterial control in a single granuloma and the outcome of infection at the level of the entire host are not well established (3).
Type 1 adaptive immunity is required to control infection at the host level (5). Activated T cells migrate to the site of infection and act as immune effectors. We distinguish three primary T cell types based on their effector function (cf Ref. 6). Proinflammatory T cells (CD4+ or CD8+) provide macrophage-activating cytokines (e.g., IFN-γ), whereas CTLs (predominantly CD8+) provide cytolytic functions to control infection (reviewed in Ref. 1). A third T cell class, regulatory T cells (Treg; reviewed in Ref. 7), are present in mouse (8) and human (9) Mtb infections, and may prevent efficient Mtb clearance by immune responses (10, 11), or may modulate local responses to control pathology. Treg are CD4+Foxp3+ cells that comprise ∼5–10% of all CD4+ T cells (12, 13). They suppress the action of proinflammatory T cells (14) through poorly understood mechanisms that may occur by cell contact, secretion of immunosuppressive cytokines (15), or both.
The proinflammatory cytokine TNF-α (TNF) is a central, multifaceted contributor to the immune response in Mtb infection produced by activated macrophages and proinflammatory T cells (16, 17, 18, 19, 20). The role of TNF is of clinical interest due to the association of anti-inflammatory TNF-blocking drugs with reactivation of latent TB in humans (21, 22). TNF is also necessary for Mtb containment in mouse models (16). TNF gene-disrupted or neutralized mice have disorganized granulomas in Mtb infections (17), underscoring the link between granuloma structure and effective containment of infection.
TNF has multiple immunological functions during infection with Mtb (Fig. 1⇓A). TNF has a direct role in immune cell recruitment via up-regulation of endothelial adhesion molecules (23), facilitating transendothelial migration of immune cells to the site of infection. TNF regulates production of chemokines by macrophages (24, 25); chemokines can further induce transendothelial migration (reviewed in Ref. 26) and coordinate recruitment (reviewed in Ref. 27) of immune cells within the tissues. TNF activates macrophages in conjunction with the cytokine IFN-γ (28, 29, 30); such activated macrophages can kill intracellular mycobacteria. TNF can also induce necrotic or apoptotic cell death in macrophages (31) that is promoted by Mtb infection (32). Fig. 1⇓A summarizes these effects.
The effects of TNF in Mtb granuloma formation are most likely related to the chemokine network induced during infection. We have identified a simplified model of chemokines based on three classes that affect recruitment of macrophages and T cells to the granuloma via binding of appropriate chemokine receptors on the cell surface (Fig. 1⇑B). The α-chemoattractant class (CXCL9, 10, and 11; formerly monokine induced by IFN-γ, IFN-γ-inducible protein-10, and IFN-inducible T cell α-chemoattractant, respectively) binds chemokine receptor CXCR3 on proinflammatory CD4+ and CD8+ T cells (33), but not regulatory T cells (34). CCL2 (formerly MCP-1) binds CCR2 on macrophages (35) and proportions of proinflammatory T cell populations (36). CCL5 (formerly RANTES) binds CCR5 on macrophages and T cells, and is involved in migration of regulatory T cells to the site of other infections (37), although this has not been demonstrated for Mtb.
Each of the four roles of TNF (cellular migration, induction of chemokine/TNF secretion, macrophage activation, and apoptosis) may contribute separately to establishing and maintaining control of Mtb infection at the level of a single granuloma. Currently, it is impossible to study these separate TNF functions using in vitro or in vivo models. In this study, we use a specific type of computational model known as an agent-based model (ABM) to study the contributions of these immune effectors on granuloma formation (illustrated in Fig. 2⇓). The power of this approach lies in the emergence of behaviors that arise from interactions between agents that would otherwise be impossible to know a priori. We build on our previously described ABM (38), which predicted the emergence of two-dimensional spherical structures (granulomas) without any rules specifying such spatial behavior to occur. We now incorporate various functions of TNF, different T cell classes, and a simple chemokine network. This type of model, which combines spatial and temporal behaviors, is particularly suited to the investigation of granulomas due to the representation of cells, cytokines, and bacteria, allowing tractable analysis of each factor independently or in combination. Analysis of an ABM reveals the specific contributions of TNF functions, alone and in combination, to control of Mtb infection and granuloma structure at the level of a single granuloma.
Materials and Methods
Supplememental methods, results, and time-lapse movies are available at http://malthus.micro.med.umich.edu/lab/movies/Granuloma2009/.
The model presented in this study is an extension of a previous ABM that captured cellular interactions leading to granuloma formation during infection with Mtb (38). The model is considered hybrid because we incorporated both discrete entities (cells) and continuous entities (chemokines, TNF, and Mtb) that interact simultaneously. ABMs are developed based on four considerations: an environment, agents that reside there, the rules that describe the agents and their interactions, and the timescales on which events are defined.
The environment represents a 2 mm × 2-mm section of lung parenchyma as a 100 × 100-square two-dimensional lattice with individual microcompartments scaled to the approximate size of a single macrophage 20 μm in diameter (39). Discrete agents move on the lattice and respond to their environment based on rules reflecting known biological activities. Bacteria and effector molecules can reside anywhere on the lattice and undergo diffusion when appropriate.
Caseation represents inflammation of, and damage to, the lung parenchyma from macrophage cell death. We note a change of terminology to caseation from necrosis in previous work (38), because strict necrosis within the granuloma is now believed to be caused by substantial neutrophil infiltration and death, whereas caseation is most likely initiated by macrophage death (J. L. Flynn, unpublished data). In the ABM, caseation is defined to occur when a threshold number of activated or infected macrophage deaths take place in a microcompartment. A final environmental feature is designation of spaces as vascular sources.
We include two types of discrete agents in the model, as follows: macrophages and T cells. As previously (20, 38), macrophage agents are either resting (Mr, uninfected), infected (Mi; have taken up bacteria), chronically infected (Mci; are unable to clear their intracellular bacterial load), or activated (Ma; can effectively kill bacteria). In contrast to our previous study (38), in which a single T cell class captured all cell behaviors, in this study we represent three distinct T cell subpopulations based on function, as follows: the Tγ class captures CD4+ and CD8+ proinflammatory T cells; Tc represent CTLs; and Treg represent regulatory T cells. In this representation, all T cells in a particular class have identical function; this is simpler than in vivo, but we capture enough detail in this representation for a qualitative representation of known T cell effects.
In addition to the discrete entities, extracellular bacteria and diffusing effector molecules (CCL2, CCL5, CXCL9/10/11, and TNF) are agents (concentrations) that are tracked continuously over time. The chemokine model used in this study is a simplification that was chosen to include a ligand for each key chemokine receptor, while minimizing the distinct chemokine classes represented to save on computation.
Cells respond to signals in the surrounding environment according to rules that represent known activities in vivo. During simulations, each agent responds depending on its state. Examples of rules include uptake of bacteria, macrophage activation by T cells, secretion of cytokines and chemokines, etc. For a full list of rules, see Supplement 1.5
At the beginning of a simulation, the grid has 105 randomly placed resident Mr that move randomly with no chemokine or cytokine present. Infection is initiated with one Mi, containing a single bacterium, placed at the center. We chose this inoculum because we assume a single inhalation event. We also compared our results using a single bacterium to one using a larger inoculum (15 bacteria within an infected macrophage). In that scenario, clearance of infection occurred less often, with different kinetics at early timepoints, but after 20 days the two infection trajectories and bacterial loads become similar from that point forward. Every 10 min of simulation time, positions and interactions between T cells and macrophages are updated, including recruitment of cells from vascular sources and secretion of TNF and chemokines. The landscape of molecular concentrations serves as the starting point for computing cytokine and chemokine diffusion for 10 min of simulation time. Cell states and interactions are then updated again, in the beginning of the next 10-min timestep in an asynchronous fashion, and the algorithm continues in this way for 200 days (2,880,000 6-s timesteps) of simulation time.
Parameter estimates and uncertainty and sensitivity analyses
It is difficult to estimate the rates and probabilities (parameters) of events occurring within the lung environment. We approximate parameter values in three ways: directly from available data, using uncertainty analysis, and model calibration with other data. Relative macrophage and T cell production were estimated from the literature (40, 41, 42, 43, 44). The macrophage CCL2 saturation point and detection threshold were estimated based on published data (45); T cell saturations are inferred from this. Treg are more sensitive to CCL5 than Tγ or Tc cells (42, 46). The resting macrophage lifespan is based on (47), we assume, a lifespan 10-fold shorter for activated macrophages. The lifespan of a T cell is based on previous description (48). The delay of T cell infiltration is based on previous description (49).
Because absolute levels of chemokine production, saturation, etc., were not known, we estimated the relative effects using calibration. Chemokine diffusion is based on previous description (50). The initial number of macrophages represents ∼1% coverage of the grid (38). The number of bacteria engulfed or killed by resting macrophages was chosen to be low, but nonzero. The killing of ingested bacteria by activated macrophages is related to the maximum number of intracellular Mtb that can exist in an infected macrophage before it is designated as chronically infected. Ntact is a guess based on physical interactions of several T cells around a single macrophage. Other parameter value estimates are discussed in supplemental material.
All other parameters not discussed above that are listed in Tables I⇓ and III⇓ were estimated using uncertainty analysis, with various parameters calibrated to ensure distinct outcomes between control and TNF-deletion cases. Parameters in Table II⇓, including production, sensitivity and saturation thresholds, and effects on recruitment for all other chemokines, were defined to preserve a relative relationship with the CCL5 parameters (dependent parameters). Thus, when Table I⇓ parameters (independent parameters) vary, Table II⇓ parameters also vary because they are proportional.
Because not all parameters can be estimated from available data, it is necessary to use uncertainty and sensitivity methods to explore possible model outcomes using different parameter values. We have developed extensive methods for performing this analysis on ABMs and applied them in this study (see Ref. 51 ; for more details see Supplement 2). Simulations using uncertainty analyses yielded a wide range of different granuloma structures and bacterial numbers within the granulomas (supplementary Fig. S1). Data from humans and nonhuman primates with TB strongly support the existence of a diverse array of granuloma types even within a single host (4, 52).
Simulated deletion and depletion of TNF activities
To examine the effect of individual TNF activities on granuloma formation and maintenance, we performed virtual deletions and depletions of relevant parameters using a baseline parameter set that leads to control of infection (Tables I–III⇑⇑⇓). Loss of activity was brought about by setting relevant probabilities to zero and/or raising relevant thresholds to an unattainable level. Virtual deletion refers to loss of the activity from the beginning of the simulation at the onset of infection. Virtual depletion refers to the loss of the activity after the establishment of a stable granuloma, 100 days postinfection. The timing of the depletion was determined by examining the results of sensitivity analysis and the baseline control scenario. Parameter sensitivities in the model stabilize by day 50 (cf, Results), suggesting that 100 days postinfection represents a reasonable time for an established, stable granuloma. Significant differences between outcome variables were determined with a mean difference test (Welch’s approximate t test) for 15 repeated simulations of each single deletion or depletion, and for 10 repeated simulations in deletion or depletion of two or more TNF activities simultaneously (for details, see Supplement 2).
Simulations with bacterial levels fixed after 100 days postinfection
To control for the effects of bacterial growth on granuloma structure, we fixed total bacterial levels (Be + Bi). Specifically, we simulated a baseline granuloma for 100 days and then fixed the total bacterial numbers to a constant level (2.75 ± 0.51 log10 Mtb) for the next 100 simulation days. This allowed exploration of the effects of TNF depletions without the confounding effects of bacterial growth. To attain a fixed Mtb population, all bacterial growth and death were prevented, with the effects of bacteria on host macrophages allowed to otherwise proceed normally. Upon exit of intracellular Mtb from macrophages due to cell lysis or death, the bacteria were forced to deposit on the single microcompartment where the macrophage resided after day 100 (unlike other simulations, in which bacteria were distributed in the entire (Moore) neighborhood of a macrophage). This step prevents a biologically unrealistic scenario that could occur in which the bacteria are shuffled to the outside of the granuloma. Note that intracellular and extracellular Mtb quantities still changed in the simulations, but the total bacterial number was conserved.
An ABM for dissection of granuloma structure and function
Computational models of the immune response to a pathogen can be deterministic (based on ordinary differential equations representing rates of change) or stochastic (based on probabilities of discrete agents and the rules governing their behavior). For the current investigation examining the contribution of various functions of TNF to granuloma structure and bacterial control in Mtb infection, we chose to use an ABM that is a hybrid model. This approach is appropriate because both spatial and temporal events must be considered when investigating the tuberculous granuloma. The ABM predicts the dynamics of Mtb infection at the level of a single granuloma.
To first identify variables that determine initial containment or maintenance of an established infection at the scale of a single granuloma, we established a reference parameter set (Tables I–III⇑⇑⇑) using uncertainty and sensitivity analyses, as outlined in Materials and Methods and Supplement 2. The baseline simulations lead to stable bacterial numbers contained within the granuloma (∼103 total bacteria) up to 200 days postinfection (Fig. 3⇓A, □). Repeated simulations show that the granuloma is organized with relatively tightly packed cells, predominantly uninfected macrophages (green agents in Fig. 3⇓B), with T cell localization at the periphery of the granuloma (pink, purple, and light blue agents in Fig. 3⇓B). The model is robust: for 15 repeated runs that established stable bacterial levels, all showed controlled infection with variable outcomes of granuloma structure (Supplement 3, Fig. S2A presents repeat simulations of the baseline scenario illustrating extent of variability).
Simulated infection with all parameters set to the control scenario, but lacking TNF (virtual TNF deletion) results in a granuloma that is irregular in shape, increased in size, and with widespread caseation (Fig. 3⇑C). Bacterial numbers are significantly higher than in the baseline scenario (Fig. 3⇑A, : ∼104 compared with 103; p < 0.01). Numbers of all macrophage and T cell populations in the model are significantly elevated in comparison with the baseline scenario within the first 20 days after infection (data not shown). Therefore, loss of TNF appears to impair early control of infection, resulting in more extensive immune cell infiltration; this corresponds to data from murine models of Mtb infection (24).
Factors that substantially contribute to control of infection within the granuloma
To identify which parameters significantly contributed to control of Mtb infection within the granuloma, we performed a global uncertainty and sensitivity analysis, varying parameters in Tables I⇑ and II⇑. Using the outcomes of this, we performed a sensitivity analysis to statistically determine which factors control bacterial numbers within the granuloma6 (Fig. 4⇓A). Approximately two-thirds of the simulations led to clearance of the infection. Some combinations of parameter values promote elimination of bacteria before granuloma formation due to apoptosis-induced killing and innate clearance by Mr (data not shown). The parameters that emerged as especially important were bacterial growth rates, T cell movement, and certain TNF-related effects. We then assessed the effects of these identified parameters on variables such as T cell functions, granuloma size, macrophage numbers, and level of caseation in the model (Table IV⇓).
The analysis indicated that growth rates of intracellular and extracellular Mtb (αBi and αBe, respectively) are positively correlated with increased bacterial numbers in the granuloma (4) (Fig. 4⇑A). This result suggests that the growth rate of mycobacteria is a virulence factor, consistent with the published data that more virulent clinical strains of Mtb grow more quickly in macrophages (53). A faster intracellular growth rate leads to increased secretion of TNF and chemokines early in infection (due to increased antigenic stimulation), and to increased T cell activity by 200 days postinfection (Table IV⇑). However, the heightened immune response is apparently incapable of controlling the infection in the face of the increased bacterial growth rate. This is recapitulated in animal models, in which increased bacterial numbers result in increased inflammation and more T cell activation, but not necessarily better control of infection (54).
Not surprisingly, the probability (Tmove) of a T cell moving to a location occupied by another cell (either a T cell or a macrophage) is significantly negatively correlated with bacterial levels (Fig. 4⇑A). A microcompartment can hold either two T cells or a T cell and a macrophage, but not two T cells and a macrophage or more than one macrophage. At 200 days, an increase in the rate of Tmove negatively impacts every measure in our model. In other words, a more tightly packed granuloma is more effective in controlling bacterial numbers, because this increases cell-cell interactions, leading to activation of infected macrophages by T cells.
As expected from the human and animal model data and our previous studies (17, 20), TNF is crucial to the outcome of infection at the level of the granuloma (Fig. 3⇑). In this analysis, the overall rate of TNF production (sTNF) was strongly negatively correlated with bacterial numbers (Fig. 4⇑A), confirming the necessity for TNF in controlling infection. Increased TNF production from the beginning of infection resulted in clearance of infection in more than one-half of the simulations. The rate of TNF secretion strongly and negatively influences most variables in the model, particularly in the long-term (see Table IV⇑), suggesting that the rate of TNF secretion from macrophages and Tγ cells (sTNF) has a global regulatory role in the system, with a major impact on immune cell and bacterial populations.
Contribution of individual TNF-dependent mechanisms to infection outcome
The original analysis confirmed a major role for TNF in control of the infection within the granuloma. In this model and in vivo, TNF has numerous functions, including inducing secretion of TNF and chemokines, activation of macrophages to a bactericidal state (in concert with IFN-γ), recruitment of cells, and apoptosis of macrophages. To assess the correlation of individual TNF-related functions with control of bacterial infection, we performed a focused sensitivity analysis, varying only the seven TNF parameters, with all other parameters at the values shown in Tables II⇑ and III⇑.
This analysis (Fig. 4⇑B) reveals positive correlations between extracellular Mtb numbers and four TNF-related parameters: τTNFact (threshold for TNF-induced activation by macrophages); τTNFapopt (threshold for TNF-induced apoptosis by macrophages); δTNF (rate of TNF degradation); and rTNF (effect of TNF on cell recruitment via transendothelial migration). Not surprisingly, the rate of TNF secretion from macrophages and Tγ cells (sTNF) is still the most significantly correlated mechanism (Fig. 4⇑B). These results suggest that multiple TNF-dependent mechanisms contribute to the observed effect of TNF on bacterial control.
The role of individual TNF-dependent mechanisms in control of infection
To identify the contribution of each TNF-dependent mechanism individually to the outcome of infection at the granuloma level, we simulated deletion and depletion of each mechanism (both single and in combination).
Virtual deletion and depletion of TNF-related macrophage activation show significantly higher levels of extracellular bacteria and caseation, with lower numbers of activated macrophage and unchanged granuloma size (Table V⇓; cf Supplement 3, Fig. S2 for all simulated granulomas). The granuloma structures resemble an intermediate between the tightly packed form of the baseline scenario and the irregular core observed with complete TNF deletion or depletion (see Fig. 3⇑). Caseation is most likely increased because bacterial numbers increase, leading to more macrophage cell death (which contributes to caseation).
Deletion and depletion of TNF-induced secretion activity (Table VI⇓) suggest different roles for this activity before and after T cell infiltration begins. Loss of secretion activity (that is, TNF-inducing TNF and chemokine production from macrophages) leads to higher levels of caseation and extracellular bacteria during a simulated deletion. No significant effects on these variables are observed during depletion, which has elevated intracellular bacteria and TNF/chemokines (compare Table VI⇓, E and F). In contrast, during both deletion and depletion (Table VI⇓, C and D), loss of TNF recruitment activity leads to a reduction of immune cells, indicating that this function of TNF is not infection stage specific.
When comparing infection initiated with 1 bacterium (above) with that of 15 bacteria as the inoculum, there are differences in the TNF analysis. While examining the role of each TNF activity in the higher inoculum scenario, we found that the effects of depleting TNF-induced secretion are not significant in the 15 bacteria case, as it was in the single bacterium scenario. This implies that aspects of TNF activities may be sensitive to the initial bacterial inoculum. We are exploring this dose-response topic further in a manuscript in preparation.
Is there a synergy or trade-off between TNF activities?
To determine the effects of interactions between specific TNF activities on granuloma structure, we performed virtual deletions and depletions of pairs and triplets of individual activities. In this section, we present results for TNF-dependent secretion, recruitment, and activation, deferring exploration of apoptosis activity to the section below.
Performing virtual double deletion/depletion of TNF-dependent recruitment and either secretion or activation activity results in the same outcome as either single depletion for secretion or activation (compare Fig. 5⇓, A and B, with Tables V⇓ and VI⇓). This suggests that TNF-dependent recruitment is not playing a major role in the control of infection at the level of the granuloma. In contrast, virtual double deletion/depletion with TNF-dependent activation and secretion yields results similar to deletion/depletion of all TNF activities in which the granuloma is larger in size, bacterial levels are increased, and there is increased caseation (compare Fig. 5⇓C with Tables V⇓ and VI⇓). A triple deletion/depletion of these three activities also results in poorly formed granulomas with high bacterial loads and caseation (Fig. 5⇓, D and H). Together these results suggest that both activation and secretion activities of TNF contribute important and distinct roles to granuloma function, formation, and/or maintenance, and that TNF-dependent recruitment is not a significant factor. Supplement 3, Fig. S3, shows all simulated granulomas at day 200 for all combinations of deletions and depletions.
For many of the results obtained to date, it is difficult to distinguish the role of bacterial numbers vs TNF activity in driving the structure of the granuloma. To address this, we performed a simulation that is impossible to do in vivo, namely, to allow the granuloma to form and then to fix the total number of bacteria present (see Materials and Methods for more details). This allows us to isolate the effects of TNF without the contribution of changing bacterial numbers. Fig. 6⇓ shows results of fixing the bacterial levels and performing virtual depletions performed at day 100 for the following: all TNF, secretion, recruitment, and activation activities, respectively. Depletion of TNF recruitment in these simulations did have an impact on the number of T cells at the site, and the granulomas were on average smaller (but not significantly so). However, the granuloma structures were not affected. In all other cases, depletion of the single TNF activities did not affect the shape or size of the granuloma once bacterial numbers were fixed. This suggests that bacterial load is the primary factor responsible for determining granuloma structure and shape.
Role of TNF-induced apoptosis
An unanticipated finding was that deletion and depletion of TNF-induced apoptosis result in effective clearance of bacteria (“Apoptosis” in Table V⇓). At the time after all bacteria were cleared, there is disrupted granuloma structure with a robust increase in cell infiltration and TNF/chemokine production (Table V⇓). Separate depletion of TNF-induced apoptosis activity from infected and uninfected macrophages shows that this phenomenon is primarily driven by uninfected macrophages, which constitute the largest macrophage subpopulation (data not shown). These data suggest that TNF-induced apoptosis of uninfected macrophages within the granuloma prevents excessive inflammation, but this leads to persistent Mtb infection.
To directly assess the contribution to granuloma structure, we fixed bacterial numbers at 100 days, as described above, and eliminated TNF-dependent apoptosis. We again observed the hyperinflammatory state described above (Fig. 7⇓A). This supports a major role for uninfected macrophages driving granuloma structure. One possible explanation is that in the absence of apoptotic cell death of macrophages, induction of TNF at the site of infection continues to induce TNF in surrounding macrophages, forming a positive feedback loop, a process normally limited by TNF-induced apoptosis in this model.
To test this possibility, we performed virtual deletion or depletion of both apoptosis and TNF-induced secretion of both TNF and chemokines (Fig. 7⇑B). Without secretion of TNF and chemokines, loss of apoptosis did not result in enhanced inflammation and resolution of infection, clearly demonstrating that the robust inflammatory response in the single deletion and depletion of apoptosis was dependent on TNF-mediated induction of TNF and chemokines from uninfected macrophages. This may be a feedback loop in which bacteria released by dying macrophages induce secretion of TNF, and higher subsequent levels of TNF and chemokines. Apoptosis of macrophages most likely prevents this TNF-induced cytokine/chemokine response by both reducing the bacteria released from macrophages and eliminating excess macrophages from the granuloma; these macrophages are the source of the cytokines and chemokines that influence the inflammatory response. Combining a loss of TNF-mediated apoptosis with loss of TNF-induced macrophage activation (Fig. 7⇑C) results in substantially worse inflammation, but no clearance of bacteria, because macrophage activation is essential for Mtb killing. Deleting or depleting both apoptosis and TNF-mediated cell recruitment (Fig. 7⇑D) is not different from the individually simulated apoptosis deletion and depletion, indicating that the observed excess inflammation is not due to changes in total recruitment of cells to the lungs.
TNF is clearly a central factor in control of Mtb infection. It has been repeatedly demonstrated in murine models that granuloma function, formation, and maintenance are dependent on TNF (24). However, TNF is a pleiotropic cytokine, and the relative contributions of its primary activities to the functional and structural characteristics of the granuloma have not been elucidated. The complex nature of TNF in the immune response to Mtb most likely accounts for the dramatic effects observed when neutralizing this cytokine in humans with latent Mtb infection (21) and in animal models (16, 19). The ABM of a tuberculous granuloma developed in this study is unique in its ability to address the roles of individual functions of TNF in granuloma formation and control of infection. The major findings from this study are as follows: 1) both TNF-induced macrophage activation and TNF-dependent secretion of chemokines and cytokines are crucial factors in control of infection within the granuloma; 2) TNF-induced apoptosis functions to reduce inflammation at the expense of impairing bacterial clearance; and 3) structural alterations in granuloma size and shape resulting from perturbation of TNF activity are directly driven by bacterial growth, and indirectly by TNF activities.
Our ABM of granuloma formation reproduces major features of infection by representing interactions of individual cell agents and molecular effectors with a representation of a growing mycobacterial population. This work was based on a previous model (38) with major extensions that include representations of TNF, a simple chemokine network, and distinct T cell subpopulations, including effector and regulatory cells. A baseline parameter set demonstrates bacterial control and variable granuloma structures, both of which are disrupted by virtual deletion or depletion of TNF. This sophisticated model allows for a spatial and temporal representation of events occurring during granuloma formation and function, allowing for exploration of this complex biological system in ways that are currently not tractable with experimental methods. A next logical step is to consider a three-dimensional spatial representation, and this may be important for making further predictions about elements within the system. In contrast, a three-dimensional granuloma model (55) gave similar results to our previous two-dimensional model (38), suggesting that the two-dimensional approach is sufficient to study many aspects of granuloma formation.
One further important simplification in our model is a reduction in the number of cell types to only those with well-characterized roles in Mtb granulomas. Multinucleate giant cells may modulate chemokine production (56) without taking up extracellular Mtb (57); dendritic cells are necessary for optimal antigenic stimulation of T cells (58, 59); foamy cells are dendritic-like cells (60) that may be a nutrient source for Mtb (61); B cells form ectopic germinal centers in Mtb-infected mouse lungs, with some role in containment (62). Each of these cell types may have important quantitative or qualitative roles, but are not sufficiently characterized to include in this model. Future work can easily incorporate these into the model when mechanistic information becomes available.
Our model provides the opportunity to study various combinations of effector functions controlled by TNF. Virtual deletion or depletion of each individual TNF-related function demonstrated that TNF-dependent macrophage activation was the primary factor involved in control of bacterial numbers, but that TNF-induced chemokine and TNF secretion also played a role in this process, probably by bringing macrophages and T cells close together in the granuloma, as well as producing more TNF in the granuloma. In contrast, loss of TNF-mediated recruitment, in which TNF acts on the vasculature to bring more cells into the lungs, had little effect on bacterial numbers, but resulted in recruitment of fewer immune cells. This is consistent with reduced recruitment in TNF-blockaded Mycobacterium bovis bacillus Calmette-Guerin infection in mice (63); lower virulence of bacillus Calmette-Guerin compared with Mtb may explain why complete loss of TNF activity did not cause a severe phenotype in that study.
Virtual elimination of both TNF-dependent macrophage activation and cytokine/chemokine secretion resulted in large granulomas with extensive caseation and lack of bacterial control that was essentially identical with the scenario of total loss of TNF, and was worse than loss of either function independently. In contrast, eliminating TNF-mediated recruitment had no additional detrimental effects when coupled with the loss of TNF-dependent macrophage activation or secretion. Thus, both macrophage activation and cytokine/chemokine secretion induced by TNF are important and distinct functions in control of bacterial numbers in the granuloma.
Based on data in the murine system, including our own data, it has been widely believed that TNF is the major factor that controls the actual formation, structure, and maintenance of a granuloma (17, 64). Humans treated with TNF-neutralizing drugs for inflammatory diseases have an increased incidence of reactivation TB. In an initial report (21), lung biopsies from a patient with disseminated TB due to TNF neutralization did not reveal granulomas, supporting the murine data. However, another human study (65) has indicated that granulomas are present in tuberculous lungs from TNF-neutralized patients, calling into question whether TNF is required for maintenance of granulomas. Indeed, data from our nonhuman primate model of TB also indicate that granulomas can form and be maintained even when TNF is neutralized (P. L. Lin and J. L. Flynn, submitted for publication).
The current virtual model provides an opportunity to test the functions of TNF that are related to granuloma function, formation, and structure. The flexibility of this model, compared with in vivo systems, allows us to hold the bacterial numbers steady while varying other parameters. This provides an opportunity to distinguish between effects of granuloma structure that are due to bacterial numbers or to TNF-dependent factors. In fact, when bacterial numbers cannot increase as a result of TNF depletion, granuloma structure is essentially unaffected by loss of TNF-dependent factors. This suggests that bacterial numbers are a driving force in the aberrations in granuloma formation and maintenance due to loss of TNF. This does not negate a role for TNF in the formation and maintenance of granulomas, but instead suggests that bacterial numbers play a greater role in this than previously believed.
One surprising finding was that deletion and depletion of TNF-induced apoptosis result in effective clearance of bacteria. Simulations predicted disrupted granuloma structure and increased cell infiltration and TNF/chemokine production in this case. Virtual depletion of TNF-induced apoptosis activity from infected or uninfected macrophages demonstrated that this phenomenon is primarily driven by uninfected macrophages. Comparing dual and single depletions, as well as the fixed bacterial numbers simulations, it became clear that the increased inflammation observed in the absence of apoptosis was due to TNF-induced secretion of chemokines and TNF from uninfected macrophages. This function of TNF enhances bacterial clearance, but can lead to excessive pathology. Thus, a role for TNF-induced apoptosis seems to be control of pathology at the expense of bacterial clearance. This provides evidence that the outcome of infection in any one granuloma is a balance of different factors, including TNF-induced functions. This result may differ if certain anti-inflammatory cytokines such as IL-10 were included in the model, which could modulate the effects of TNF; this possibility has been proposed previously (66).
The size of the initial bacterial inoculum may have some effects on progression and containment of Mtb infection, particularly in cases with immunological deficiencies (e.g., Ref. 67). We explored the effects of increasing the bacterial load in the first infected macrophage from 1 bacterium to 15. Cell and bacterial kinetics were different preceding T cell infiltration at 20 days postinfection, but became very similar after (data not shown). The primary difference in dose as it relates to TNF activities is for TNF-induced secretion of chemokines and TNF. For the low inoculum, depleting this activity significantly affected aspects of the granuloma (Table VI⇓), in which for the higher inoculum, no significant effects for the depletion were observed (data not shown). A very high inoculum, above the small range we tested in this study, would most likely have dramatic effects on the outcome of infection, as demonstrated in our previous studies (6, 20), although the effect may not manifest at the level of a single granuloma. We have a manuscript in preparation exploring the role of initial dose in a more detailed fashion.
In this work, we have used an advanced mathematical modeling approach to identify the relative contribution of different TNF-dependent activities in granuloma formation and function. Model results predicted mechanisms involved in control of both bacterial numbers within the granuloma as well as inflammation. These mechanisms can now serve as putative targets for vaccine and treatment strategies. The approach applied in this study contributes groundwork for the use of computational approaches in identifying biological pathways that can be targeted for modulation of TB.
We thank members of J.C.J.R.’s PhD thesis committee (Gary Huffnagle, Jennifer Linderman, Alex Ninfa, and Joel Swanson); Shlomo Ta'asan and other members of the Center for Modeling Pulmonary Immunity; P. Ling Lin; and Steven Kunkel and Simeone Marino for helpful comments.
The authors have no financial conflict of interest.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
↵1 This work was supported by National Institutes of Health Grants HL68526 (to D.E.K. and J.L.F.), NO1 AI50018 (to J.L.F. and D.E.K.), HL72682 (to D.E.K.), and LM00902701 (to D.E.K. and J.L.F.); the F.G. Novy Fellowship (to J.C.J.R.); and the Ellison Foundation (to J.L.F.).
↵2 Current address: Department of Bioengineering, Rice University, Houston, TX 77251.
↵3 Address correspondence and reprint requests to Dr. Denise E. Kirschner, Department of Microbiology and Immunology, University of Michigan Medical School, 6730 Medical Science Building II, Ann Arbor, MI 48109-0620. E-mail address:
↵4 Abbreviations used in this paper: TB, tuberculosis; ABM, agent-based model; Ma, activated macrophage; Mci, chronically infected macrophage; Mi, infected macrophage; Mr, resting macrophage; Mtb, Mycobacterium tuberculosis; Tmove, probability of T cell movement onto an occupied location; sTNF, overall rate of TNF production; Treg, regulatory T cell.
↵5 The online version of this article contains supplemental material.
↵6 This result differs slightly from our previous results in Segovia-Juarez et al. (38 ), which predicted that intracellular growth rates are transiently negatively correlated with extracellular Mtb numbers between days 30 and 150 postinfection. This discrepancy is due to a peak in chronically infected macrophage bursting in that model, which is not reproduced in this study, because we hold the initial number of macrophages constant. This allows uncertainty analysis to have identical initial conditions between different parameter sets.
- Received July 15, 2008.
- Accepted January 5, 2009.
- Copyright © 2009 by The American Association of Immunologists, Inc.