In Vitro Granuloma Models of Tuberculosis: Potential and Challenges

In Vitro Granuloma Models of Tuberculosis: Potential and Challenges Abstract Despite intensive research efforts, several fundamental disease processes for tuberculosis (TB) remain poorly understood. A central enigma is that host immunity is necessary to control disease yet promotes transmission by causing lung immunopathology. Our inability to distinguish these processes makes it challenging to design rational novel interventions. Elucidating basic immune mechanisms likely requires both in vivo and in vitro analyses, since Mycobacterium tuberculosis is a highly specialized human pathogen. The classic immune response is the TB granuloma organized in three dimensions within extracellular matrix. Several groups are developing cell culture granuloma models. In January 2018, NIAID convened a workshop, entitled “3-D Human in vitro TB Granuloma Model” to advance the field. Here, we summarize the arguments for developing advanced TB cell culture models and critically review those currently available. We discuss how integrating complementary approaches, specifically organoids and mathematical modeling, can maximize progress, and conclude by discussing future challenges and opportunities. granuloma, tuberculosis, tissue culture models WHY ADVANCED CELL CULTURE MODELS MAY BE REQUIRED? Mycobacterium tuberculosis kills more people than any other single infectious disease and new interventions to control the ongoing pandemic are urgently required [1]. From the Food and Drug Administration and other regulatory agencies’ perspective, this necessitates preclinical data that reliably predicts efficacy in humans. A fundamental challenge for the field is the complexity of the host-pathogen interaction, particularly within multicellular tissue granulomas [2]. Granulomas are organized cellular aggregates containing multiple cells, primarily with a central core of mature macrophages, surrounded by T cells, B cells, and fibroblasts, and are proposed to form in response to a persistent stimulus. Although granulomas have been well described since the invention of the microscope, mechanisms that regulate cellular dynamics, behavior, and maintenance are only recently being fully understood. For example, while the granuloma has traditionally thought to be necessary to limit infection, more recent data from the zebrafish model suggest that it may facilitate mycobacterial proliferation and dissemination [2], and the molecular determinants of macrophage reprogramming are only recently emerging [3]. Specific cellular events typical of tuberculosis granulomas, such as the formation of multinucleate giant cells or development of central necrotic caseation, are incompletely understood. In this regard, development of more advanced in vitro systems to study human tuberculosis granulomas may be worthwhile, but conversely it could be argued that human M. tuberculosis infection is so prolonged and complex that only in vivo models can provide meaningful results [4]. Herein, we first outline potential benefits of in vitro granuloma models (Table 1). We discuss what an optimal model may include, and how to resolve the inherent tension between complexity, tractability, and throughput. We summarize recent progress and review how parallel developments in organ-on-a-chip systems and mathematical modeling may feed into the field. We address the need for validation of in vitro models with in vivo-derived information. Finally, we conclude by suggesting a roadmap for potential future developments and outcomes. Table 1. Research Areas Amenable to In Vitro Granuloma Models Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response View Large Table 1. Research Areas Amenable to In Vitro Granuloma Models Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response View Large POTENTIAL BENEFITS OF A HUMAN MODEL A fundamental issue within the tuberculosis field is our failure to fully understand the determinants of control versus disease progression. Cell culture systems permit mechanistic and dynamic investigations that are not possible in patients and may be challenging in animal models. M. tuberculosis is predominantly a human pathogen, so infection of another host makes assumptions that fundamental biological pathways are common to both. M. tuberculosis and humans have undergone a prolonged coevolution for approximately 70 000 years [5], thus human systems are essential to confirm or refute findings in other model systems. Therefore, an advanced human tuberculosis cell culture model that recapitulates core features of human infection would permit dissection of relevant basic biological processes and also the testing of new diagnostic, therapeutic, and vaccine interventions. An early model was developed by Altare [6], which was used to investigate specific aspects of host immunity to M. tuberculosis [7, 8], and helped inform the more recently developed models. For an optimal model we propose that the pathogen should be virulent M. tuberculosis, because although M. bovis has 99.95% genetic homology to M. tuberculosis, it does not cause the same disease [9]. Primary human cells should be used because cell lines or cells from other species do not reliably predict responses of primary human cells. The composition should include mononuclear phagocytes and lymphocytes as they constitute the prominent cell types of tuberculosis granulomas, as well as fibroblasts and epithelial cells [2]. The model should permit 2- or, preferably, 3-dimensional organization, because the human immune response is spatially organized in 3 dimensions [10], and incorporate physiological extracellular matrix, because this regulates host cell survival and M. tuberculosis growth [11, 12]. Longer-term experiments are preferred, because the host-pathogen interaction is prolonged [4]. Current models already permit longer experiments than standard cultures (up to 4 weeks), but are arguably still relatively short compared to human infection and should be extended as feasible. Furthermore, an ideal system would permit the continued addition of immune cells, because granulomas are dynamic environments with the recruitment of new cells [13]. Readouts should be multiparameter, incorporating both host and pathogen physiology, because any single intervention will likely have multiple reciprocal effects on host and pathogen. The approach is ideally modular, so that individual components or factors can be varied within the system to test specific hypotheses. Finally, it should be possible to modulate the environment over time, because conditions change dynamically, for example reduced oxygen tension and altered drug pharmacokinetics [14, 15], and the model should allow for interventions of different conditions and resultant analyses. However, there is no point in adding complexity to a model system without a specific goal, therefore an equally pertinent question is what is the minimal composition of a model system to permit investigation of fundamental disease mechanisms that are predictive of the human response. Organoid models are teaching us that it may not be necessary to incorporate all elements to obtain results that recapitulate the in vivo situation [16]. Increasing complexity may detract from the tractability, with a balance ranging from genuine high throughput to very intricate but low replicate numbers. Equally relevant to defining the minimum requirement of a model system is the determination of what approach is appropriate for in vivo validation. Finally, each model system should allow for integration of comorbidities, for example HIV, diabetes, and cigarette smoke exposure, as well as assessment of responses in the very young and old [1]. However, an open question is when to introduce these variables into the model. CELL CULTURE SYSTEMS OF TUBERCULOSIS GRANULOMAS DEVELOPED TO DATE Collagen Matrix Model of M. tuberculosis Dormancy The 3-dimensional model developed by Kapoor et al [17] uses human peripheral blood mononuclear cells (PBMCs) in an extracellular matrix to form spatiotemporal 3- dimensional structures and microscopic granulomatous aggregates in response to virulent M. tuberculosis. This model demonstrates features of human tuberculosis, for example multinucleated giant cell formation, increase in CD4+CD25+ T cells, decrease in activated macrophages, and increase in cytokine and chemokine secretion by immune cells in response to M. tuberculosis (Figure 1). Critically, the model demonstrates development of M. tuberculosis latency and reactivation upon immune suppression caused by anti-tumor necrosis factor-alpha (anti-TNF-α) treatment. Figure 1. View largeDownload slide The collagen matrix model: (A) infected peripheral blood mononuclear cells (PBMCs); (B) uninfected PBMCs; (C) H & E staining showing multinucleated giant cells (arrows); (D) fluorescent staining of granulomas sections with 4′,6-diamidino-2-phenylindole (DAPI, nuclear stain), CD68 (macrophage marker, red), and CD3 (T cells, green) monoclonal antibodies. Reproduced from [17]. Permission to reprint this figure provided by ©2013 Kapoor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. Figure 1. View largeDownload slide The collagen matrix model: (A) infected peripheral blood mononuclear cells (PBMCs); (B) uninfected PBMCs; (C) H & E staining showing multinucleated giant cells (arrows); (D) fluorescent staining of granulomas sections with 4′,6-diamidino-2-phenylindole (DAPI, nuclear stain), CD68 (macrophage marker, red), and CD3 (T cells, green) monoclonal antibodies. Reproduced from [17]. Permission to reprint this figure provided by ©2013 Kapoor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. Therefore, this model can be used to understand the host-pathogen interaction during latency and resuscitation because it displays fundamental characteristics of latency, such as a nonreplicating state [18], development of resistance to rifampicin, loss of acid-fastness, and accumulation of lipid bodies [19, 20]. Consequently, it has potential to identify agents active against dormant bacilli, a key consideration in the development of short-course regimes [1]. Limitations of the model include relatively low throughput, technical challenges of adding further cells to permit modeling dynamics over time, and the requirement of collagenase to release cells from the collagen matrix for downstream analyses. Multicellular Lung Tissue Model Lerm and colleagues [21] introduced M. tuberculosis into an existing human in vitro lung tissue model in order to study early granuloma formation. A collagen matrix supported by a filter membrane forms a scaffold for a human fibroblast cell line, which grows and differentiates before addition of primary macrophages/monocytes and a human epithelial cell line (Figure 2). After the cells have formed a tissue, the apical side is exposed to air, which causes the epithelial cells to secrete mucus. This organotypic mucosa is representative of lung tissue, both in anatomical and functional aspects [22]. To achieve an M. tuberculosis infection representative of human tuberculosis, macrophages carrying the bacilli are introduced as “Trojan horses.” Monocytes, which easily migrate in the model [21], cluster at the infected macrophages to form granuloma-like structures. To avoid artefacts due to physical sectioning of the structures for interrogation, cryosectioning can be replaced by optical sectioning using confocal microscopy, which also has the advantage that a microtome is not needed in the BSL3 facility [23]. The relevance of using such a biomimetic human tissue model has been emphasized [24]. This model has allowed the group to validate observations made in animal models. Figure 2. View largeDownload slide The human in vitro lung tissue model. Cells are sequentially layered onto a collagen matrix and then infected macrophages are added to model the early events of human lung infection. Figure 2. View largeDownload slide The human in vitro lung tissue model. Cells are sequentially layered onto a collagen matrix and then infected macrophages are added to model the early events of human lung infection. Granulomas are highly dynamic rather than static structures [25] and one important question raised is whether granulomas restrict or promote M. tuberculosis growth [2]. A recent study in zebrafish demonstrated that M. marinum hijacks newly recruited macrophages to disseminate in the tissue [2]. This observation could be confirmed with M. tuberculosis in the human lung tissue model, showing that M. tuberculosis uses its virulence factors to trigger granuloma formation, pointing towards a bacterial advantage [21]. Further, matrix metalloproteinases (MMPs) secreted by host cells in infected tissue as a consequence of mycobacterial virulence factors can promote granuloma formation in zebrafish [2] and are implicated in human tuberculosis pathology [26, 27]. These observations have been investigated and confirmed using the human in vitro lung tissue model, dissecting the set of MMPs induced by M. tuberculosis infection, and demonstrating that MMP inhibition reduces both granuloma pathology and bacterial load [28]. Furthermore, these findings are consistent with subsequent reports of the benefit of MMP inhibition in a mouse model of tuberculosis [29]. Limitations of this model include the difficulty of adding other immune cells such as lymphocytes due to MHC incompatibility, or neutrophils, which the developers have attempted, and so it primarily models the macrophage-M. tuberculosis interaction. In addition, translation to high throughput is challenging. The model has potential to study diverse aspects of host protection, such as epigenetic modulation to improve protection, and as a secondary assay for testing novel tuberculosis drugs, which underpins its usefulness for applied tuberculosis research. Granuloma Model to Assess the Impact of the Human Immune Response The Schlesinger laboratory group has created a human PBMC-based granuloma model with 2 goals in mind: (1) to address how the human immune status may dictate early granuloma formation and bacterial response; and (2) to enable tractability for potential translational applications (Figure 3) [30]. The model uses human PBMCs isolated from naive or latent tuberculosis-infected individuals, autologous serum, and virulent M. tuberculosis, and is being interrogated to discriminate host and bacterial determinants in individuals with and without latent tuberculosis-infection. The model demonstrates the significant influence of immune memory on granuloma formation, bacterial survival, lymphocyte proliferation, pro- and antiinflammatory cytokines, and lipid body accumulation. Moreover, there is a specific transcriptional signature of M. tuberculosis associated with survival depending on the host immune status. Specifically, with latent tuberculosis-infected individuals, M. tuberculosis converts to a latency signature early (within 7 days) indicating early adaptation to the granuloma environment. Figure 3. View largeDownload slide The host immune response impact on granulomas. Human peripheral blood mononuclear cells from either naive or latent tuberculosis-infected individuals are incubated with autologous serum and Mycobacterium tuberculosis. The model has shown that host immune status has significant impacts on granuloma formation and function, and bacterial responses. Abbreviations: IFN-γ, interferon gamma; IL, interleukin; LAM, lipoarabinomannan; TNF, tumor necrosis factor. Figure 3. View largeDownload slide The host immune response impact on granulomas. Human peripheral blood mononuclear cells from either naive or latent tuberculosis-infected individuals are incubated with autologous serum and Mycobacterium tuberculosis. The model has shown that host immune status has significant impacts on granuloma formation and function, and bacterial responses. Abbreviations: IFN-γ, interferon gamma; IL, interleukin; LAM, lipoarabinomannan; TNF, tumor necrosis factor. Limitations of the published model include the absence of fibroblasts and matrix, which have since been added and found to influence the kinetics of granuloma formation and stability. The model also lacks the continual influx of mononuclear phagocytes to maintain the dynamic structures over longer periods of time. HIV has been added to the model providing insight into its effects on the nature and timing of granuloma formation and dissolution. The model allows for comparative analysis with other granulomatous diseases such as sarcoidosis [31] and is scalable for throughput analysis of potential therapeutic compounds. Therefore, further development and evolution of this system can investigate basic biology, therapeutics, and also the effect of comorbidities. Bioelectrospray 3-Dimensional Model The Southampton tuberculosis group have developed a system based on cell encapsulation within microspheres, using cross-linking of alginate when immersed in a gelling bath of calcium chloride [32]. The resulting microspheres have a matrix and cellular composition that is highly tractable (Figure 4), while cells can readily be released from the spheres for downstream analysis by dissolving the microspheres in ethylenediaminetetraacetic acid (EDTA) or sodium citrate. The group has used the system to investigate diverse aspects of the host-pathogen interaction and translational application. First, comparison of spheres with or without collagen provided evidence that the extracellular matrix regulates the host-pathogen interaction, as first suggested from transgenic mouse studies [11]. Subsequently, the group used the system to investigate different aspects of the host immune response, such as studying spheres exposed to different cytokines or with augmented M. tuberculosis-responsive T cells [12]. The ability to incorporate different cell types into multiple spheres, and then study the effect for over 21 days, is a potentially powerful application to determine protective versus pathological immune responses. Importantly, M. tuberculosis is pyrazinamide sensitive when in 3-dimensional microspheres, but not in standard 2-dimensional culture or broth, demonstrating that M. tuberculosis is under similar stress to that encountered under in vivo conditions [33]. Because the cells and bacteria are held within the microspheres, the model can be readily adapted to microfluidic pharmacokinetic modeling, and accelerated M. tuberculosis killing can be shown with increasing concentrations of rifampicin [33]. Finally, the group has used the model to study the effect of MMP inhibition with doxycycline to limit tuberculosis-driven immunopathology [27]. Figure 4. View largeDownload slide The bioelectrospray microsphere model: immunoaugmentation with Mycobacterium tuberculosis-specific T cells. A, Cellular aggregates within microspheres after 14 days. Scale bar 20 μm. B, Microspheres imaged after 4 days show early granuloma formation (orange). C, Addition of either ESAT-6 responsive T cells (red) or CFP-10 responsive T cells (blue) increases M. tuberculosis (Mtb) growth compared to infected peripheral blood mononuclear cells without supplemented T cells (black). Reproduced from [12] . Permission to reprint this figure by 2017 Tezera et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. *P < .05; ***P < .001. Abbreviation: Con, control; RLU, relative light unit. Figure 4. View largeDownload slide The bioelectrospray microsphere model: immunoaugmentation with Mycobacterium tuberculosis-specific T cells. A, Cellular aggregates within microspheres after 14 days. Scale bar 20 μm. B, Microspheres imaged after 4 days show early granuloma formation (orange). C, Addition of either ESAT-6 responsive T cells (red) or CFP-10 responsive T cells (blue) increases M. tuberculosis (Mtb) growth compared to infected peripheral blood mononuclear cells without supplemented T cells (black). Reproduced from [12] . Permission to reprint this figure by 2017 Tezera et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. *P < .05; ***P < .001. Abbreviation: Con, control; RLU, relative light unit. Limitations of the model are broadly similar to those outlined above, including the further addition of immune cells (simulating cell recruitment [13]), which is challenging once cells are encapsulated within the microspheres. Further development is required to model the multiple microenvironments that M. tuberculosis is thought to reside in during human infection [4]. For example, dual encapsulation would permit modeling of the caseous central core, while generation of larger spheres would provide an oxygen gradient from the periphery to a hypoxic core [14]. Modulation of the microsphere composition will be required to permit cellular ingress to mimic recruitment to granulomas. Lessons From Animal Models of Tuberculosis Granuloma Formation The models outlined above demonstrate the diverse potential of in vitro modeling using human cells, but at this point all lack the complexity and chronic host-pathogen interaction that occur in human disease. Therefore, animal modeling will continue to be an essential component of fully dissecting the host-pathogen interaction and findings from animal model systems must be used to further inform the development of human systems. The classically described granuloma has a caseous center surrounded by an inner myeloid and an outer lymphocytic layer [2]. Eventually, the granuloma becomes necrotic and/or fibrotic, and may lead to mineralization or cavitation. Signaling cascades involving myeloid cells and interferon-gamma from T cells contrive to eliminate M. tuberculosis within the lesion, although sterilization is rarely achieved. Thus, the granuloma environment permits mechanisms that both promote and inhibit bacterial killing. Specialized granuloma architecture of human lung tuberculosis is not modeled in the prototypical mouse granuloma, although mouse models that allow the unstable or stable development of necrotic or fibrotic lesions have recently been developed [34]. Murine granulomas also do not permit immune control in a latent state. Guinea pigs, rabbits, and macaques develop human-like necrotic and organized granulomas, and these develop hypoxia, especially in the necrotic regions [34]. Rabbits have been used, under the right conditions, to study cavitary lesions. Animal models have raised important questions about how specific mechanisms prevalent within lung granulomas can drive the balance of M. tuberculosis killing and survival. As one example, upon coming into contact with M. tuberculosis, myeloid cells intensely express indoleamine dioxygenase (IDO), a tryptophan catabolic enzyme [35]. Dr Kaushal’s work shows that IDO may inhibit optimal antituberculosis T-cell responses and in vivo blockade of this host pathway improves granuloma-specific killing of M. tuberculosis by invoking stronger adaptive responses and permitting lymphocytes access to the core of the lesion, where M. tuberculosis-infected macrophages are present. Thus, immunosuppressive pathways may be prevalent in animal, and potentially human, lung granulomas. IDO has been detected in human patients [36]. Because M. tuberculosis promotes a robust Th1 response resulting in chronic granulomatous inflammation, it is counterintuitive for M. tuberculosis to be deliberately immunogenic as the resulting immune response could eliminate infection and cause tissue damage [37]. Thus, M. tuberculosis induction of novel regulatory mechanisms like IDO could potentiate its survival in the face of this immune stress. Using zebrafish, Tobin and colleagues demonstrate that granuloma formation is accompanied by reprogramming events driven by E-cadherin creating cellular tight junctions [3]. Inhibition of this signaling pathway caused lesions to become disordered, with greater immune cell access to the granuloma-resident bacilli and eventually better clearance [3]. Thus IDO and E-cadherin represent 2 of likely several pathways that prevent efficient killing of M. tuberculosis in the granulomas. Understanding fundamental mechanisms of such signaling events will require reciprocal interchange between in vivo and in vitro models. HOW CAN PARALLEL ADVANCES IN OTHER FIELDS INFORM TUBERCULOSIS GRANULOMA MODELS? Organ on a Chip The last 2 decades have seen considerable research efforts to leverage microfabrication technologies, initially developed for the semiconductor industry, to create new types of in vitro cell culture models. Using microfabricated patterns and fluidic channels that can be engineered at the size scale of single cells, these advanced in vitro systems have demonstrated unprecedented capabilities to control the cellular microenvironment with high spatiotemporal precision and to present cultured cells with various types of biochemical and mechanical signals in a physiologically relevant context [38–40]. Recent advances in this microsystems approach have led to a new wave of microengineered cell culture models known as “organs-on-chips” (also known as microphysiological systems) designed to mimic microarchitecture, dynamic environment, and integrated biological function of complex human physiological systems [38, 41, 42]. As the proof-of-principle demonstration of this technology, a human breathing lung-on-a-chip model provides a great example of how living human cells can be combined with a synthetically designed culture environment to reverse engineer the salient features of a complex organ in vitro (Figure 5) [43–45]. By enabling coculture of human alveolar epithelial cells and lung microvascular endothelial cells on the opposite sides of a thin porous membrane undergoing cyclic stretch, this microdevice replicates the lung alveolar-capillary interface and its dynamic mechanical activity during breathing (Figure 5B). Importantly, this system also offers unique capabilities to model complex organ-level physiological functions such as protective immune responses to bacterial infection and environmental exposures (Figure 5C). Similar approaches have been applied successfully to emulating the functional units of other organs such as the liver, heart, bone, kidney, brain, intestine, bone marrow, and placenta [41]. Figure 5. View largeDownload slide A human breathing lung-on-a-chip. A, The alveolar system is modeled in a microfluidic device consisting of 2 overlapping parallel microchannels separated by a thin porous membrane. B, The alveolar-capillary interface is created in this system by culturing human alveolar epithelial cells and lung microvascular endothelial cells on either side of the membrane. To mimic breathing, cyclic vacuum is applied to the hollow chambers adjacent to the cell culture channels to stretch the membrane in the lateral direction. C, Introduction of Escherichia coli into the alveolar compartment of this model induces adhesion (top row) and transmigration (middle row) of neutrophils flowing in the lower vascular chamber. The recruited neutrophils then phagocytose the bacteria (bottom row). Portions of figure from [43] Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Hsin HY, Ingber DE. Reconstituting organ-level lung functions on a chip. Science 2010; 328:1662–8. Reprinted with permission from AAAS. Figure 5. View largeDownload slide A human breathing lung-on-a-chip. A, The alveolar system is modeled in a microfluidic device consisting of 2 overlapping parallel microchannels separated by a thin porous membrane. B, The alveolar-capillary interface is created in this system by culturing human alveolar epithelial cells and lung microvascular endothelial cells on either side of the membrane. To mimic breathing, cyclic vacuum is applied to the hollow chambers adjacent to the cell culture channels to stretch the membrane in the lateral direction. C, Introduction of Escherichia coli into the alveolar compartment of this model induces adhesion (top row) and transmigration (middle row) of neutrophils flowing in the lower vascular chamber. The recruited neutrophils then phagocytose the bacteria (bottom row). Portions of figure from [43] Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Hsin HY, Ingber DE. Reconstituting organ-level lung functions on a chip. Science 2010; 328:1662–8. Reprinted with permission from AAAS. One critical aspect of organ-on-a-chip technology that has garnered attention recently is the possibility of developing specialized in vitro models of complex disease processes. From the perspective of tuberculosis research, this is an area of significant potential that needs further exploration for modeling tuberculosis granulomas. In particular, to precisely control and manipulate cells and their microenvironmental cues may be instrumental in recapitulating granuloma complexity. For example, microdevices can be designed to contain 2 or more layers of interconnected yet individually addressable cell culture chambers in order to coculture multiple relevant cell types (eg, infected macrophages, epithelial cells, T and B cells, fibroblasts) in physiologically relevant arrangements and to modulate their environment both spatially and temporally. Such devices would provide a robust platform to mimic cellular heterogeneity and granuloma 3-dimensional structural organization, thus facilitating mechanistic investigation of complex intercellular interactions in the development and progression of tuberculosis. Further, such systems may be useful to study healing mechanisms within the lung and tissue regeneration, addressing the often-overlooked issue of pulmonary scarring that follows tuberculosis and leads to long-term morbidity. Recent advances in organ-on-a-chip technology make it possible to engineer complex 3-dimensional networks of self-assembled perfusable blood vessels by replicating the process of vasculogenesis and angiogenesis [46, 47], which could be integrated into an in vitro granuloma model to allow for continued recruitment of blood-borne immune cells, offering a means to reconstitute the dynamically replenished environment of in vivo granulomas. When constructed with human cells and tissues, organ-on-chip models of granulomas may provide a basis for novel preclinical research platforms for identification and validation of new therapeutic targets and high-content screening of lead compounds for tuberculosis [48]. These types of systems may serve as a significant contributor to timely and cost-effective translation of research discoveries for tuberculosis. Reciprocal Interchange Between In Vitro and Mathematical Modeling Several mathematical and computational models have been developed to describe various aspects of tuberculosis disease, spanning from bacterial metabolic scale to human population scale [49–53]. These models have helped inform experimental studies and expand our understanding of the mechanisms underlying tuberculosis disease. This raises the question: what role can mathematical and computational models play in the development and application of in vitro tuberculosis granuloma models? First, in vitro models are relatively easily manipulatable in model inputs, can measure more outputs at high resolution, and can easily collect longitudinal data. Therefore, the wealth of data produced by in vitro systems could contribute to development of computational models that represent the biology with a high degree of detail and confidence. Second, computational models that are well calibrated to the experimental systems could help address the tractability question for the ideal in vitro granuloma model. Computational models can be used to quickly and cost-effectively screen large numbers of interventions, thereby narrowing the treatment design space to be explored and the number of interventions to be experimentally tested in model systems. Third, it might not be feasible to develop the ideal in vitro system outlined above. Some models might be better suited for some studies than others (eg, varying drug concentrations versus including multiple cell types), or types of measurements (long-term dynamics versus high-resolution spatial data). Computational models could help integrate data from multiple in vitro systems, thereby extending their individual predictive capabilities. For example, computational methods have successfully integrated data from various in vitro cell proliferation, differentiation, and death assays to predict interventions for pulmonary fibrosis [54]. In short, using computational approaches to complement and integrate granuloma model systems could accelerate their development and application. FUTURE DEVELOPMENTS AND POTENTIAL BENEFITS The authors believe that advanced human granuloma cell culture models will make significant contributions to our understanding of tuberculosis pathogenesis going forward. The close coevolution of host and pathogen means that studying the natural pairing is necessary to understand this persistent human infection, and such cellular systems permit the mechanistic and dynamic investigations required to fully dissect granuloma biogenesis and allow for multiple types of interrogations. As one example, the combination of advanced models with single cell sequencing and other “omics” approaches would permit unique dissection of the host-pathogen interaction in a human system. Similarly, 3-dimensional live cell imaging of granulomas over time may provide new insights into granuloma biology, just as it has been so informative in the zebrafish model of M. marinum [2]. Granuloma models may uncover novel regulatory pathways and therapeutic approaches, such as host-directed therapies, while predicting both the beneficial and potentially harmful outcomes of each intervention in clinical trials. Similarly, if protective innate or adaptive immune responses can be fully characterized in these models, these will inform new vaccination strategies and their efficacies when used in clinical studies. The primary challenge is defining the key attributes of any in vitro model that are required to reflect events in vivo. Simply developing ever more complex models is wasteful of resources if they are not aimed at answering specific pathophysiological questions. Development of granuloma systems must be cross-correlated with events in patients and in animal models to ensure relevance to human disease. Thus, a multidisciplinary approach bringing together advanced cell culture modelers with bioengineers and mathematical modelers, in tandem with in vivo models, is needed to bridge the requirements of complexity, tractability, and throughput. Ultimately, we will only control tuberculosis if we fully understand its pathogenesis. Further advancing cell culture models and related technologies can serve as a central pillar of that effort in bridging in vivo experimentation and clinical studies. Notes Acknowledgments. The authors thank our fellow workshop presenters, Eusondia Arnett, Daniel Barber, Louis Joslyn, Smita Kulkarni, Lucie Low, Matyas Sandor, and Ankur Singh, as well as Liku Tezera and Magdalena Bielecka for their insights and lively discussion. Financial support. This work was supported by Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Department of Health and Human Services (contract number HHSN272201600001G—Research Support Services for the Division of AIDS support for this publication and workshop 3-D Human In Vitro TB Granuloma Model, January 2018). Support was also provided by the Medical Research Council (grant number MR/P023754/1 to P. E.); the NIH (grant numbers 1DP2HL127720-01 to D. H., R33AI102239 to P. E., UL1TR001070 to L. S. S.); the National Science Foundation (grant number CMMI:15-48571 to D. H.); and the Swedish Research Council (grant number 2014–02289 to M. L.). Potential conflicts of interest. D. H. owns shares in Emulate Inc., consults for the company, and serves on its scientific advisory board. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Wallis RS , Maeurer M , Mwaba P , et al. Tuberculosis–advances in development of new drugs, treatment regimens, host-directed therapies, and biomarkers . Lancet Infect Dis 2016 ; 16 : e34 – 46 . Google Scholar Crossref Search ADS PubMed 2. Pagán AJ , Ramakrishnan L . The formation and function of granulomas . Annu Rev Immunol 2018 ; 36 : 639 – 65 . Google Scholar Crossref Search ADS PubMed 3. Cronan MR , Beerman RW , Rosenberg AF , et al. 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Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Infectious Diseases Oxford University Press

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Abstract

Abstract Despite intensive research efforts, several fundamental disease processes for tuberculosis (TB) remain poorly understood. A central enigma is that host immunity is necessary to control disease yet promotes transmission by causing lung immunopathology. Our inability to distinguish these processes makes it challenging to design rational novel interventions. Elucidating basic immune mechanisms likely requires both in vivo and in vitro analyses, since Mycobacterium tuberculosis is a highly specialized human pathogen. The classic immune response is the TB granuloma organized in three dimensions within extracellular matrix. Several groups are developing cell culture granuloma models. In January 2018, NIAID convened a workshop, entitled “3-D Human in vitro TB Granuloma Model” to advance the field. Here, we summarize the arguments for developing advanced TB cell culture models and critically review those currently available. We discuss how integrating complementary approaches, specifically organoids and mathematical modeling, can maximize progress, and conclude by discussing future challenges and opportunities. granuloma, tuberculosis, tissue culture models WHY ADVANCED CELL CULTURE MODELS MAY BE REQUIRED? Mycobacterium tuberculosis kills more people than any other single infectious disease and new interventions to control the ongoing pandemic are urgently required [1]. From the Food and Drug Administration and other regulatory agencies’ perspective, this necessitates preclinical data that reliably predicts efficacy in humans. A fundamental challenge for the field is the complexity of the host-pathogen interaction, particularly within multicellular tissue granulomas [2]. Granulomas are organized cellular aggregates containing multiple cells, primarily with a central core of mature macrophages, surrounded by T cells, B cells, and fibroblasts, and are proposed to form in response to a persistent stimulus. Although granulomas have been well described since the invention of the microscope, mechanisms that regulate cellular dynamics, behavior, and maintenance are only recently being fully understood. For example, while the granuloma has traditionally thought to be necessary to limit infection, more recent data from the zebrafish model suggest that it may facilitate mycobacterial proliferation and dissemination [2], and the molecular determinants of macrophage reprogramming are only recently emerging [3]. Specific cellular events typical of tuberculosis granulomas, such as the formation of multinucleate giant cells or development of central necrotic caseation, are incompletely understood. In this regard, development of more advanced in vitro systems to study human tuberculosis granulomas may be worthwhile, but conversely it could be argued that human M. tuberculosis infection is so prolonged and complex that only in vivo models can provide meaningful results [4]. Herein, we first outline potential benefits of in vitro granuloma models (Table 1). We discuss what an optimal model may include, and how to resolve the inherent tension between complexity, tractability, and throughput. We summarize recent progress and review how parallel developments in organ-on-a-chip systems and mathematical modeling may feed into the field. We address the need for validation of in vitro models with in vivo-derived information. Finally, we conclude by suggesting a roadmap for potential future developments and outcomes. Table 1. Research Areas Amenable to In Vitro Granuloma Models Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response View Large Table 1. Research Areas Amenable to In Vitro Granuloma Models Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response Research Area Specific Example Diagnostics and therapeutics Biomarker identification and development Early screening for potential therapeutics (antibiotics and host-directed therapies) Mechanistic insight into new therapeutic targets (receptors, signaling pathways, etc.) Dynamics of granulomas Longitudinal studies (eg, daily imaging) that facilitate assessment of host and microbial changes over time Ease of interrogation/manipulation of the model to study the Mycobacterium tuberculosis-host interaction Human relevance Human cell-based observations to complement animal models, aiding in predictive accuracy prior to testing in humans Facilitate analysis of comorbidities (eg, HIV, diabetes, smoking) Heterogeneity Allow for tractable analysis of the heterogenous tuberculosis response in humans to therapies and vaccines (naive, latent tuberculosis infected, and active tuberculosis patient groups) Vaccine development Identification and development of new vaccine adjuvants; host response to adjuvants Define protective versus pathological components of the host immune response View Large POTENTIAL BENEFITS OF A HUMAN MODEL A fundamental issue within the tuberculosis field is our failure to fully understand the determinants of control versus disease progression. Cell culture systems permit mechanistic and dynamic investigations that are not possible in patients and may be challenging in animal models. M. tuberculosis is predominantly a human pathogen, so infection of another host makes assumptions that fundamental biological pathways are common to both. M. tuberculosis and humans have undergone a prolonged coevolution for approximately 70 000 years [5], thus human systems are essential to confirm or refute findings in other model systems. Therefore, an advanced human tuberculosis cell culture model that recapitulates core features of human infection would permit dissection of relevant basic biological processes and also the testing of new diagnostic, therapeutic, and vaccine interventions. An early model was developed by Altare [6], which was used to investigate specific aspects of host immunity to M. tuberculosis [7, 8], and helped inform the more recently developed models. For an optimal model we propose that the pathogen should be virulent M. tuberculosis, because although M. bovis has 99.95% genetic homology to M. tuberculosis, it does not cause the same disease [9]. Primary human cells should be used because cell lines or cells from other species do not reliably predict responses of primary human cells. The composition should include mononuclear phagocytes and lymphocytes as they constitute the prominent cell types of tuberculosis granulomas, as well as fibroblasts and epithelial cells [2]. The model should permit 2- or, preferably, 3-dimensional organization, because the human immune response is spatially organized in 3 dimensions [10], and incorporate physiological extracellular matrix, because this regulates host cell survival and M. tuberculosis growth [11, 12]. Longer-term experiments are preferred, because the host-pathogen interaction is prolonged [4]. Current models already permit longer experiments than standard cultures (up to 4 weeks), but are arguably still relatively short compared to human infection and should be extended as feasible. Furthermore, an ideal system would permit the continued addition of immune cells, because granulomas are dynamic environments with the recruitment of new cells [13]. Readouts should be multiparameter, incorporating both host and pathogen physiology, because any single intervention will likely have multiple reciprocal effects on host and pathogen. The approach is ideally modular, so that individual components or factors can be varied within the system to test specific hypotheses. Finally, it should be possible to modulate the environment over time, because conditions change dynamically, for example reduced oxygen tension and altered drug pharmacokinetics [14, 15], and the model should allow for interventions of different conditions and resultant analyses. However, there is no point in adding complexity to a model system without a specific goal, therefore an equally pertinent question is what is the minimal composition of a model system to permit investigation of fundamental disease mechanisms that are predictive of the human response. Organoid models are teaching us that it may not be necessary to incorporate all elements to obtain results that recapitulate the in vivo situation [16]. Increasing complexity may detract from the tractability, with a balance ranging from genuine high throughput to very intricate but low replicate numbers. Equally relevant to defining the minimum requirement of a model system is the determination of what approach is appropriate for in vivo validation. Finally, each model system should allow for integration of comorbidities, for example HIV, diabetes, and cigarette smoke exposure, as well as assessment of responses in the very young and old [1]. However, an open question is when to introduce these variables into the model. CELL CULTURE SYSTEMS OF TUBERCULOSIS GRANULOMAS DEVELOPED TO DATE Collagen Matrix Model of M. tuberculosis Dormancy The 3-dimensional model developed by Kapoor et al [17] uses human peripheral blood mononuclear cells (PBMCs) in an extracellular matrix to form spatiotemporal 3- dimensional structures and microscopic granulomatous aggregates in response to virulent M. tuberculosis. This model demonstrates features of human tuberculosis, for example multinucleated giant cell formation, increase in CD4+CD25+ T cells, decrease in activated macrophages, and increase in cytokine and chemokine secretion by immune cells in response to M. tuberculosis (Figure 1). Critically, the model demonstrates development of M. tuberculosis latency and reactivation upon immune suppression caused by anti-tumor necrosis factor-alpha (anti-TNF-α) treatment. Figure 1. View largeDownload slide The collagen matrix model: (A) infected peripheral blood mononuclear cells (PBMCs); (B) uninfected PBMCs; (C) H & E staining showing multinucleated giant cells (arrows); (D) fluorescent staining of granulomas sections with 4′,6-diamidino-2-phenylindole (DAPI, nuclear stain), CD68 (macrophage marker, red), and CD3 (T cells, green) monoclonal antibodies. Reproduced from [17]. Permission to reprint this figure provided by ©2013 Kapoor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. Figure 1. View largeDownload slide The collagen matrix model: (A) infected peripheral blood mononuclear cells (PBMCs); (B) uninfected PBMCs; (C) H & E staining showing multinucleated giant cells (arrows); (D) fluorescent staining of granulomas sections with 4′,6-diamidino-2-phenylindole (DAPI, nuclear stain), CD68 (macrophage marker, red), and CD3 (T cells, green) monoclonal antibodies. Reproduced from [17]. Permission to reprint this figure provided by ©2013 Kapoor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. Therefore, this model can be used to understand the host-pathogen interaction during latency and resuscitation because it displays fundamental characteristics of latency, such as a nonreplicating state [18], development of resistance to rifampicin, loss of acid-fastness, and accumulation of lipid bodies [19, 20]. Consequently, it has potential to identify agents active against dormant bacilli, a key consideration in the development of short-course regimes [1]. Limitations of the model include relatively low throughput, technical challenges of adding further cells to permit modeling dynamics over time, and the requirement of collagenase to release cells from the collagen matrix for downstream analyses. Multicellular Lung Tissue Model Lerm and colleagues [21] introduced M. tuberculosis into an existing human in vitro lung tissue model in order to study early granuloma formation. A collagen matrix supported by a filter membrane forms a scaffold for a human fibroblast cell line, which grows and differentiates before addition of primary macrophages/monocytes and a human epithelial cell line (Figure 2). After the cells have formed a tissue, the apical side is exposed to air, which causes the epithelial cells to secrete mucus. This organotypic mucosa is representative of lung tissue, both in anatomical and functional aspects [22]. To achieve an M. tuberculosis infection representative of human tuberculosis, macrophages carrying the bacilli are introduced as “Trojan horses.” Monocytes, which easily migrate in the model [21], cluster at the infected macrophages to form granuloma-like structures. To avoid artefacts due to physical sectioning of the structures for interrogation, cryosectioning can be replaced by optical sectioning using confocal microscopy, which also has the advantage that a microtome is not needed in the BSL3 facility [23]. The relevance of using such a biomimetic human tissue model has been emphasized [24]. This model has allowed the group to validate observations made in animal models. Figure 2. View largeDownload slide The human in vitro lung tissue model. Cells are sequentially layered onto a collagen matrix and then infected macrophages are added to model the early events of human lung infection. Figure 2. View largeDownload slide The human in vitro lung tissue model. Cells are sequentially layered onto a collagen matrix and then infected macrophages are added to model the early events of human lung infection. Granulomas are highly dynamic rather than static structures [25] and one important question raised is whether granulomas restrict or promote M. tuberculosis growth [2]. A recent study in zebrafish demonstrated that M. marinum hijacks newly recruited macrophages to disseminate in the tissue [2]. This observation could be confirmed with M. tuberculosis in the human lung tissue model, showing that M. tuberculosis uses its virulence factors to trigger granuloma formation, pointing towards a bacterial advantage [21]. Further, matrix metalloproteinases (MMPs) secreted by host cells in infected tissue as a consequence of mycobacterial virulence factors can promote granuloma formation in zebrafish [2] and are implicated in human tuberculosis pathology [26, 27]. These observations have been investigated and confirmed using the human in vitro lung tissue model, dissecting the set of MMPs induced by M. tuberculosis infection, and demonstrating that MMP inhibition reduces both granuloma pathology and bacterial load [28]. Furthermore, these findings are consistent with subsequent reports of the benefit of MMP inhibition in a mouse model of tuberculosis [29]. Limitations of this model include the difficulty of adding other immune cells such as lymphocytes due to MHC incompatibility, or neutrophils, which the developers have attempted, and so it primarily models the macrophage-M. tuberculosis interaction. In addition, translation to high throughput is challenging. The model has potential to study diverse aspects of host protection, such as epigenetic modulation to improve protection, and as a secondary assay for testing novel tuberculosis drugs, which underpins its usefulness for applied tuberculosis research. Granuloma Model to Assess the Impact of the Human Immune Response The Schlesinger laboratory group has created a human PBMC-based granuloma model with 2 goals in mind: (1) to address how the human immune status may dictate early granuloma formation and bacterial response; and (2) to enable tractability for potential translational applications (Figure 3) [30]. The model uses human PBMCs isolated from naive or latent tuberculosis-infected individuals, autologous serum, and virulent M. tuberculosis, and is being interrogated to discriminate host and bacterial determinants in individuals with and without latent tuberculosis-infection. The model demonstrates the significant influence of immune memory on granuloma formation, bacterial survival, lymphocyte proliferation, pro- and antiinflammatory cytokines, and lipid body accumulation. Moreover, there is a specific transcriptional signature of M. tuberculosis associated with survival depending on the host immune status. Specifically, with latent tuberculosis-infected individuals, M. tuberculosis converts to a latency signature early (within 7 days) indicating early adaptation to the granuloma environment. Figure 3. View largeDownload slide The host immune response impact on granulomas. Human peripheral blood mononuclear cells from either naive or latent tuberculosis-infected individuals are incubated with autologous serum and Mycobacterium tuberculosis. The model has shown that host immune status has significant impacts on granuloma formation and function, and bacterial responses. Abbreviations: IFN-γ, interferon gamma; IL, interleukin; LAM, lipoarabinomannan; TNF, tumor necrosis factor. Figure 3. View largeDownload slide The host immune response impact on granulomas. Human peripheral blood mononuclear cells from either naive or latent tuberculosis-infected individuals are incubated with autologous serum and Mycobacterium tuberculosis. The model has shown that host immune status has significant impacts on granuloma formation and function, and bacterial responses. Abbreviations: IFN-γ, interferon gamma; IL, interleukin; LAM, lipoarabinomannan; TNF, tumor necrosis factor. Limitations of the published model include the absence of fibroblasts and matrix, which have since been added and found to influence the kinetics of granuloma formation and stability. The model also lacks the continual influx of mononuclear phagocytes to maintain the dynamic structures over longer periods of time. HIV has been added to the model providing insight into its effects on the nature and timing of granuloma formation and dissolution. The model allows for comparative analysis with other granulomatous diseases such as sarcoidosis [31] and is scalable for throughput analysis of potential therapeutic compounds. Therefore, further development and evolution of this system can investigate basic biology, therapeutics, and also the effect of comorbidities. Bioelectrospray 3-Dimensional Model The Southampton tuberculosis group have developed a system based on cell encapsulation within microspheres, using cross-linking of alginate when immersed in a gelling bath of calcium chloride [32]. The resulting microspheres have a matrix and cellular composition that is highly tractable (Figure 4), while cells can readily be released from the spheres for downstream analysis by dissolving the microspheres in ethylenediaminetetraacetic acid (EDTA) or sodium citrate. The group has used the system to investigate diverse aspects of the host-pathogen interaction and translational application. First, comparison of spheres with or without collagen provided evidence that the extracellular matrix regulates the host-pathogen interaction, as first suggested from transgenic mouse studies [11]. Subsequently, the group used the system to investigate different aspects of the host immune response, such as studying spheres exposed to different cytokines or with augmented M. tuberculosis-responsive T cells [12]. The ability to incorporate different cell types into multiple spheres, and then study the effect for over 21 days, is a potentially powerful application to determine protective versus pathological immune responses. Importantly, M. tuberculosis is pyrazinamide sensitive when in 3-dimensional microspheres, but not in standard 2-dimensional culture or broth, demonstrating that M. tuberculosis is under similar stress to that encountered under in vivo conditions [33]. Because the cells and bacteria are held within the microspheres, the model can be readily adapted to microfluidic pharmacokinetic modeling, and accelerated M. tuberculosis killing can be shown with increasing concentrations of rifampicin [33]. Finally, the group has used the model to study the effect of MMP inhibition with doxycycline to limit tuberculosis-driven immunopathology [27]. Figure 4. View largeDownload slide The bioelectrospray microsphere model: immunoaugmentation with Mycobacterium tuberculosis-specific T cells. A, Cellular aggregates within microspheres after 14 days. Scale bar 20 μm. B, Microspheres imaged after 4 days show early granuloma formation (orange). C, Addition of either ESAT-6 responsive T cells (red) or CFP-10 responsive T cells (blue) increases M. tuberculosis (Mtb) growth compared to infected peripheral blood mononuclear cells without supplemented T cells (black). Reproduced from [12] . Permission to reprint this figure by 2017 Tezera et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. *P < .05; ***P < .001. Abbreviation: Con, control; RLU, relative light unit. Figure 4. View largeDownload slide The bioelectrospray microsphere model: immunoaugmentation with Mycobacterium tuberculosis-specific T cells. A, Cellular aggregates within microspheres after 14 days. Scale bar 20 μm. B, Microspheres imaged after 4 days show early granuloma formation (orange). C, Addition of either ESAT-6 responsive T cells (red) or CFP-10 responsive T cells (blue) increases M. tuberculosis (Mtb) growth compared to infected peripheral blood mononuclear cells without supplemented T cells (black). Reproduced from [12] . Permission to reprint this figure by 2017 Tezera et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License. *P < .05; ***P < .001. Abbreviation: Con, control; RLU, relative light unit. Limitations of the model are broadly similar to those outlined above, including the further addition of immune cells (simulating cell recruitment [13]), which is challenging once cells are encapsulated within the microspheres. Further development is required to model the multiple microenvironments that M. tuberculosis is thought to reside in during human infection [4]. For example, dual encapsulation would permit modeling of the caseous central core, while generation of larger spheres would provide an oxygen gradient from the periphery to a hypoxic core [14]. Modulation of the microsphere composition will be required to permit cellular ingress to mimic recruitment to granulomas. Lessons From Animal Models of Tuberculosis Granuloma Formation The models outlined above demonstrate the diverse potential of in vitro modeling using human cells, but at this point all lack the complexity and chronic host-pathogen interaction that occur in human disease. Therefore, animal modeling will continue to be an essential component of fully dissecting the host-pathogen interaction and findings from animal model systems must be used to further inform the development of human systems. The classically described granuloma has a caseous center surrounded by an inner myeloid and an outer lymphocytic layer [2]. Eventually, the granuloma becomes necrotic and/or fibrotic, and may lead to mineralization or cavitation. Signaling cascades involving myeloid cells and interferon-gamma from T cells contrive to eliminate M. tuberculosis within the lesion, although sterilization is rarely achieved. Thus, the granuloma environment permits mechanisms that both promote and inhibit bacterial killing. Specialized granuloma architecture of human lung tuberculosis is not modeled in the prototypical mouse granuloma, although mouse models that allow the unstable or stable development of necrotic or fibrotic lesions have recently been developed [34]. Murine granulomas also do not permit immune control in a latent state. Guinea pigs, rabbits, and macaques develop human-like necrotic and organized granulomas, and these develop hypoxia, especially in the necrotic regions [34]. Rabbits have been used, under the right conditions, to study cavitary lesions. Animal models have raised important questions about how specific mechanisms prevalent within lung granulomas can drive the balance of M. tuberculosis killing and survival. As one example, upon coming into contact with M. tuberculosis, myeloid cells intensely express indoleamine dioxygenase (IDO), a tryptophan catabolic enzyme [35]. Dr Kaushal’s work shows that IDO may inhibit optimal antituberculosis T-cell responses and in vivo blockade of this host pathway improves granuloma-specific killing of M. tuberculosis by invoking stronger adaptive responses and permitting lymphocytes access to the core of the lesion, where M. tuberculosis-infected macrophages are present. Thus, immunosuppressive pathways may be prevalent in animal, and potentially human, lung granulomas. IDO has been detected in human patients [36]. Because M. tuberculosis promotes a robust Th1 response resulting in chronic granulomatous inflammation, it is counterintuitive for M. tuberculosis to be deliberately immunogenic as the resulting immune response could eliminate infection and cause tissue damage [37]. Thus, M. tuberculosis induction of novel regulatory mechanisms like IDO could potentiate its survival in the face of this immune stress. Using zebrafish, Tobin and colleagues demonstrate that granuloma formation is accompanied by reprogramming events driven by E-cadherin creating cellular tight junctions [3]. Inhibition of this signaling pathway caused lesions to become disordered, with greater immune cell access to the granuloma-resident bacilli and eventually better clearance [3]. Thus IDO and E-cadherin represent 2 of likely several pathways that prevent efficient killing of M. tuberculosis in the granulomas. Understanding fundamental mechanisms of such signaling events will require reciprocal interchange between in vivo and in vitro models. HOW CAN PARALLEL ADVANCES IN OTHER FIELDS INFORM TUBERCULOSIS GRANULOMA MODELS? Organ on a Chip The last 2 decades have seen considerable research efforts to leverage microfabrication technologies, initially developed for the semiconductor industry, to create new types of in vitro cell culture models. Using microfabricated patterns and fluidic channels that can be engineered at the size scale of single cells, these advanced in vitro systems have demonstrated unprecedented capabilities to control the cellular microenvironment with high spatiotemporal precision and to present cultured cells with various types of biochemical and mechanical signals in a physiologically relevant context [38–40]. Recent advances in this microsystems approach have led to a new wave of microengineered cell culture models known as “organs-on-chips” (also known as microphysiological systems) designed to mimic microarchitecture, dynamic environment, and integrated biological function of complex human physiological systems [38, 41, 42]. As the proof-of-principle demonstration of this technology, a human breathing lung-on-a-chip model provides a great example of how living human cells can be combined with a synthetically designed culture environment to reverse engineer the salient features of a complex organ in vitro (Figure 5) [43–45]. By enabling coculture of human alveolar epithelial cells and lung microvascular endothelial cells on the opposite sides of a thin porous membrane undergoing cyclic stretch, this microdevice replicates the lung alveolar-capillary interface and its dynamic mechanical activity during breathing (Figure 5B). Importantly, this system also offers unique capabilities to model complex organ-level physiological functions such as protective immune responses to bacterial infection and environmental exposures (Figure 5C). Similar approaches have been applied successfully to emulating the functional units of other organs such as the liver, heart, bone, kidney, brain, intestine, bone marrow, and placenta [41]. Figure 5. View largeDownload slide A human breathing lung-on-a-chip. A, The alveolar system is modeled in a microfluidic device consisting of 2 overlapping parallel microchannels separated by a thin porous membrane. B, The alveolar-capillary interface is created in this system by culturing human alveolar epithelial cells and lung microvascular endothelial cells on either side of the membrane. To mimic breathing, cyclic vacuum is applied to the hollow chambers adjacent to the cell culture channels to stretch the membrane in the lateral direction. C, Introduction of Escherichia coli into the alveolar compartment of this model induces adhesion (top row) and transmigration (middle row) of neutrophils flowing in the lower vascular chamber. The recruited neutrophils then phagocytose the bacteria (bottom row). Portions of figure from [43] Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Hsin HY, Ingber DE. Reconstituting organ-level lung functions on a chip. Science 2010; 328:1662–8. Reprinted with permission from AAAS. Figure 5. View largeDownload slide A human breathing lung-on-a-chip. A, The alveolar system is modeled in a microfluidic device consisting of 2 overlapping parallel microchannels separated by a thin porous membrane. B, The alveolar-capillary interface is created in this system by culturing human alveolar epithelial cells and lung microvascular endothelial cells on either side of the membrane. To mimic breathing, cyclic vacuum is applied to the hollow chambers adjacent to the cell culture channels to stretch the membrane in the lateral direction. C, Introduction of Escherichia coli into the alveolar compartment of this model induces adhesion (top row) and transmigration (middle row) of neutrophils flowing in the lower vascular chamber. The recruited neutrophils then phagocytose the bacteria (bottom row). Portions of figure from [43] Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Hsin HY, Ingber DE. Reconstituting organ-level lung functions on a chip. Science 2010; 328:1662–8. Reprinted with permission from AAAS. One critical aspect of organ-on-a-chip technology that has garnered attention recently is the possibility of developing specialized in vitro models of complex disease processes. From the perspective of tuberculosis research, this is an area of significant potential that needs further exploration for modeling tuberculosis granulomas. In particular, to precisely control and manipulate cells and their microenvironmental cues may be instrumental in recapitulating granuloma complexity. For example, microdevices can be designed to contain 2 or more layers of interconnected yet individually addressable cell culture chambers in order to coculture multiple relevant cell types (eg, infected macrophages, epithelial cells, T and B cells, fibroblasts) in physiologically relevant arrangements and to modulate their environment both spatially and temporally. Such devices would provide a robust platform to mimic cellular heterogeneity and granuloma 3-dimensional structural organization, thus facilitating mechanistic investigation of complex intercellular interactions in the development and progression of tuberculosis. Further, such systems may be useful to study healing mechanisms within the lung and tissue regeneration, addressing the often-overlooked issue of pulmonary scarring that follows tuberculosis and leads to long-term morbidity. Recent advances in organ-on-a-chip technology make it possible to engineer complex 3-dimensional networks of self-assembled perfusable blood vessels by replicating the process of vasculogenesis and angiogenesis [46, 47], which could be integrated into an in vitro granuloma model to allow for continued recruitment of blood-borne immune cells, offering a means to reconstitute the dynamically replenished environment of in vivo granulomas. When constructed with human cells and tissues, organ-on-chip models of granulomas may provide a basis for novel preclinical research platforms for identification and validation of new therapeutic targets and high-content screening of lead compounds for tuberculosis [48]. These types of systems may serve as a significant contributor to timely and cost-effective translation of research discoveries for tuberculosis. Reciprocal Interchange Between In Vitro and Mathematical Modeling Several mathematical and computational models have been developed to describe various aspects of tuberculosis disease, spanning from bacterial metabolic scale to human population scale [49–53]. These models have helped inform experimental studies and expand our understanding of the mechanisms underlying tuberculosis disease. This raises the question: what role can mathematical and computational models play in the development and application of in vitro tuberculosis granuloma models? First, in vitro models are relatively easily manipulatable in model inputs, can measure more outputs at high resolution, and can easily collect longitudinal data. Therefore, the wealth of data produced by in vitro systems could contribute to development of computational models that represent the biology with a high degree of detail and confidence. Second, computational models that are well calibrated to the experimental systems could help address the tractability question for the ideal in vitro granuloma model. Computational models can be used to quickly and cost-effectively screen large numbers of interventions, thereby narrowing the treatment design space to be explored and the number of interventions to be experimentally tested in model systems. Third, it might not be feasible to develop the ideal in vitro system outlined above. Some models might be better suited for some studies than others (eg, varying drug concentrations versus including multiple cell types), or types of measurements (long-term dynamics versus high-resolution spatial data). Computational models could help integrate data from multiple in vitro systems, thereby extending their individual predictive capabilities. For example, computational methods have successfully integrated data from various in vitro cell proliferation, differentiation, and death assays to predict interventions for pulmonary fibrosis [54]. In short, using computational approaches to complement and integrate granuloma model systems could accelerate their development and application. FUTURE DEVELOPMENTS AND POTENTIAL BENEFITS The authors believe that advanced human granuloma cell culture models will make significant contributions to our understanding of tuberculosis pathogenesis going forward. The close coevolution of host and pathogen means that studying the natural pairing is necessary to understand this persistent human infection, and such cellular systems permit the mechanistic and dynamic investigations required to fully dissect granuloma biogenesis and allow for multiple types of interrogations. As one example, the combination of advanced models with single cell sequencing and other “omics” approaches would permit unique dissection of the host-pathogen interaction in a human system. Similarly, 3-dimensional live cell imaging of granulomas over time may provide new insights into granuloma biology, just as it has been so informative in the zebrafish model of M. marinum [2]. Granuloma models may uncover novel regulatory pathways and therapeutic approaches, such as host-directed therapies, while predicting both the beneficial and potentially harmful outcomes of each intervention in clinical trials. Similarly, if protective innate or adaptive immune responses can be fully characterized in these models, these will inform new vaccination strategies and their efficacies when used in clinical studies. The primary challenge is defining the key attributes of any in vitro model that are required to reflect events in vivo. Simply developing ever more complex models is wasteful of resources if they are not aimed at answering specific pathophysiological questions. Development of granuloma systems must be cross-correlated with events in patients and in animal models to ensure relevance to human disease. Thus, a multidisciplinary approach bringing together advanced cell culture modelers with bioengineers and mathematical modelers, in tandem with in vivo models, is needed to bridge the requirements of complexity, tractability, and throughput. Ultimately, we will only control tuberculosis if we fully understand its pathogenesis. Further advancing cell culture models and related technologies can serve as a central pillar of that effort in bridging in vivo experimentation and clinical studies. Notes Acknowledgments. The authors thank our fellow workshop presenters, Eusondia Arnett, Daniel Barber, Louis Joslyn, Smita Kulkarni, Lucie Low, Matyas Sandor, and Ankur Singh, as well as Liku Tezera and Magdalena Bielecka for their insights and lively discussion. Financial support. This work was supported by Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Department of Health and Human Services (contract number HHSN272201600001G—Research Support Services for the Division of AIDS support for this publication and workshop 3-D Human In Vitro TB Granuloma Model, January 2018). Support was also provided by the Medical Research Council (grant number MR/P023754/1 to P. E.); the NIH (grant numbers 1DP2HL127720-01 to D. H., R33AI102239 to P. E., UL1TR001070 to L. S. 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Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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The Journal of Infectious DiseasesOxford University Press

Published: Mar 31, 2019

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