You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Differences in Reactivation of Tuberculosis Induced from Anti-TNF Treatments Are Based on Bioavailability in Granulomatous Tissue}
Article:
Abstract:
The immune response to Mycobacterium tuberculosis( Mtb) infection is complex. Experimental evidence has revealed that tumor necrosis factor( TNF) plays a major role in host defense against Mtb in both active and latent phases of infection. TNF-neutralizing drugs used to treat inflammatory disorders have been reported to increase the risk of tuberculosis( TB), in accordance with animal studies. The present study takes a computational approach toward characterizing the role of TNF in protection against the tubercle bacillus in both active and latent infection. We extend our previous mathematical models to investigate the roles and production of soluble( sTNF) and transmembrane TNF( tmTNF). We analyze effects of anti-TNF therapy in virtual clinical trials( VCTs) by simulating two of the most commonly used therapies, anti-TNF antibody and TNF receptor fusion, predicting mechanisms that explain observed differences in TB reactivation rates. The major findings from this study are that bioavailability of TNF following anti-TNF therapy is the primary factor for causing reactivation of latent infection and that sTNF—even at very low levels—is essential for control of infection. Using a mathematical model, it is possible to distinguish mechanisms of action of the anti-TNF treatments and gain insights into the role of TNF in TB control and pathology. Our study suggests that a TNF-modulating agent could be developed that could balance the requirement for reduction of inflammation with the necessity to maintain resistance to infection and microbial diseases. Alternatively, the dose and timing of anti-TNF therapy could be modified. Anti-TNF therapy will likely lead to numerous incidents of primary TB if used in areas where exposure is likely.
Introduction:
Control of Mycobacterium tuberculosis( Mtb) infection is a result of a successful immune response that requires priming and activation of antigen-specific CD4+ and CD8+ T lymphocytes, recruitment of cells to the infection site( typically the lung), and production of cytokines, some of whose role is to activate macrophages. This leads to inhibition or killing of some but not all bacilli. Immunological structures( granulomas) form in the lung in response to persistent antigen and cytokine and chemokine signals. In 95% of infected hosts, M. tuberculosis( Mtb) persists without causing symptoms or disease. Latent infection can subsequently reactivate to cause active TB. Experimental evidence has revealed that tumor necrosis factor( TNF) plays a major role in host defense against Mtb in both the active and chronic phases of infection[1–4]. TNF action increases the phagocytosis by macrophages and enhances mycobacterial killing in concert with IFN-γ[3, 5]. TNF is crucial in recruitment of inflammatory cells, stimulating chemokine production[6] and inducing adhesion molecules on vascular endothelium[7]. Table S1 summarizes data regarding TNF in Mtb murine models. TNF is a crucial component of both antibacterial protection and the inflammatory immune response. TNF-deficient mice exhibit disorganized granulomas, altered tissue pathology, high bacterial loads, and reduced survival[2, 3]. TNF also possesses tissue-injuring properties that manifest in clinical settings including inflammation, auto-immune diseases, and transplant rejections[8, 9]. In TB patients, peripheral increases in TNF have been implicated in clinical worsening[10]. In the absence of TNF signaling, disruption of granulomatous formation as well as dissolution of granulomas during chronic infection occurred, resulting in death of the mice[1, 4, 11]. TNF is initially a transmembrane( tmTNF) protein that undergoes cleavage by the specific metalloproteinase TNF-converting enzyme( TACE) to form a soluble trimer[soluble tumor necrosis factor( sTNF)][12]. Both forms of TNF function by binding to one of two receptors, TNFR1( TNFRp55) and TNFR2( TNFRp75)[13]. It was reasoned that transmembrane ligands of TNF superfamily might elicit bidirectional signals( reverse signaling)[14]. That hypothesis was supported by data describing potential receptor-like properties of tmTNF[15]. The majority of reverse signaling described in lymphocytes is stimulatory[16, 17], whereas monocytes are mainly inhibited in their effector functions[18–20]. For further details, we review TNF biology( sTNF and tmTNF, receptors, reverse signaling, and the role of lymphotoxin) in Text S1. Known effects of sTNF and tmTNF on macrophages and T cells are summarized in Table 1. Several biologic inhibitors( antibodies and receptor fusion molecules) have been developed that interfere with TNF activity and are used to control inflammation in diseases such as rheumatoid arthritis[21, 22] and Crohn's disease[23, 24]. The importance of TNF in control of TB is highlighted by an increased susceptibility of these patients to TB reactivation[25, 26]. The incidence of TB in individuals receiving antibody appears to be higher than in those treated with receptor fusion molecule[27], suggesting that there are differences in the mode of action of these agents. Alternatively, the dose and timing of anti-TNF therapy could be modified. The present study takes a theoretical approach toward characterizing the role of TNF in protection against the tubercle bacillus in both active and latent infection. We extend our previous models[28–31] to investigate the roles and production of sTNF versus tmTNF. To explore the effects of TNF blockade, we study two anti-TNF agents, a TNF-neutralizing antibody and a soluble p75 TNF receptor fusion( TNFR2Fc). Using a mathematical model, it is possible to distinguish mechanisms of action of the anti-TNF treatments and gain insights into the role of TNF in TB control and pathology.
Discussion:
The major findings from this study are that bioavailability of TNF following anti-TNF therapy is the primary factor for causing reactivation of latent infection, that anti-TNF therapy will likely lead to numerous incidents of primary TB if used in areas where exposure is likely, and that sTNF—even at very low levels—is essential for control of infection. Our model predictions( see Figure 3) recapitulate recent murine studies that tmTNF is sufficient to provide acute but not long-term control of Mtb infection[42, 43]. We predict that >2% of total TNF needs to be released in soluble form to control acute infection and maintain latency. Bacterial loads increase as the percentage σ of total TNF cleaved is decreased, i. e., allowing more tmTNF in the system. We use the model to analyze the effects of anti-TNF therapy by simulating anti-TNF antibody and TNFR2Fc. The reported measure unit for a steady state or average concentration of anti-TNF drugs in serum is on the order of μg/ml. Data on soluble TNFRs concentration in serum are on the order of ng/ml[45]. We use and predict concentrations of sTNF within the order of pg/mL based on our granuloma homogenate data( see Text S2). Given these predicted and reported concentrations, both treatments can systemically neutralize most if not all TNF. We can speculate that both TNFR2Fc and antibody will likely neutralize most if not all TNF at the granuloma site, if each penetrates granulomatous tissue equally well. An alternative way to interpret bioavailability is in terms of drug penetration into granulomatous tissues. However, our studies in murine models suggest that anti-TNF antibody penetrates or remains in the granuloma at higher levels than receptor fusion molecules[46]. The pharmacodynamic differences between these two agents include increased dissociation rate of TNF from TNFR2Fc compared with antibody[47]. Whether these differences play a role inside the granuloma is not known. However, one can imagine that increased dissociation in the context of high concentrations of endogenous TNF receptors could lead to better TNF signaling in the granuloma. The VCT simulations suggest that TNF bioavailability is the main factor leading to reactivation by anti-TNF treatments in latently infected patients. Reactivation always occurs if both drugs penetrate the granuloma equally well( TNF bioavailability less than 20%). High bacterial load at treatment initiation increases the likelihood of reactivation. This suggests that a complete regimen of antibiotic treatment for Mtb infection prior to anti-TNF treatment could reduce the risk of reactivation. If TNF bioavailability is equally affected by the two treatments, differential cell level losses induced by anti-TNF antibody therapy accounts for higher reactivation rates: activated CD8+ T cells and MA loss are not compensated by the apparently beneficial effect of MI loss. We speculate that the intracellular bacteria released after MI death induced by antibody binding to tmTNF( whether dependent on tmTNF reverse signaling or complement cascade) can only facilitate bacterial clearance by the host and does not enhance dissemination. Further, our results show that the longer patients are exposed to anti-TNF drugs through longer duration treatment protocols, the risk of reactivation increases. If infection with Mtb occurs after treatment is initiated, chances of developing active infection are very high if we assume reasonable levels of drug penetration into lungs( TNF bioavailability <50%). This is particularly important if anti-TNF treatments are implemented in regions of the world where infection risk is elevated. Bacteria grow uncontrolled when both sTNF and tmTNF are depleted( anti-TNF antibody therapy). These data suggest that tmTNF plays a key role in controlling active infection, where tmTNF preserves a subset of the beneficial mechanisms of TNF while lacking detrimental effects. Our predictions and recent experimental data[42] support the hypothesis that selective targeting of sTNF may offer several advantages over complete blockade of TNF in the treatment of chronic inflammatory diseases. Current studies in both murine and NHP animal models by our group are now following up on these predictions. Our recent data from a mouse model showed that treatment with anti-TNF Ab in the chronic phase rapidly resulted in fulminant TB, while treatment with an etanercept-like molecule( receptor fusion) allowed mice to maintain control of the infection[46]. In contrast, following treatment with either antibody or receptor fusion during initial infection caused mice to succumb rapidly to the infection. This clearly demonstrated, in a mouse model, that there are differences between the two TNF neutralizing drugs depending on the phase of infection[46]. Our studies in NHP model are ongoing. Finally, this work focuses on reactivation based on bacteria harbored in the lungs. But data exist suggesting that bacteria do not reside only in granulomas, but in other places in the body during latency. For example, recent data by Neyrolles et al.[48] support the presence of nonreplicating bacteria in adipose tissue during latent TB infection. In the future we can consider the role of reseeding of the lungs from other body sites as a possible feature in reactivation.
Tuberculosis ( TB ) is the leading cause of death due to infectious disease in the world today . It is estimated that 2 billion people are currently infected , and although most people have latent infection , reactivation occurs due to factors such as HIV-1 and aging . Antibiotic treatments exist; however , there is still no cure and the current vaccine has proven to be unreliable . Experimental science has uncovered a plethora of immune factors that help the host control infection and maintain latency . One such factor , tumor necrosis factor alpha ( TNF ) , is a protein that facilitates cell–cell communication during an inflammatory immune response . Animal models have shown that TNF is necessary for control of TB infection . Different types of anti-TNF drugs were developed for patients with non-TB related inflammatory diseases such as rheumatoid arthritis and Crohn's disease . Some of these patients who had latent TB suffered reactivation , especially with one drug type . Because these studies cannot be performed in the mouse , and nonhuman primates are expensive , we developed a computational model to perform virtual clinical trials ( VCTs ) that predicted why reactivation occurs and why it happens differentially between the two classes of drugs tested . We make recommendations on how this issue can be combated .