Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis

Pulmonary epithelial wound healing and the immune system. Mathematical modeling and bifurcation analysis of a bistable system

Respiratory diseases such as asthma, acute respiratory distress syndrome (ARDS), influenza or COVID-19 often directly target the epithelium. Elevated immune levels and a 'cytokine storm' are directly associated with defective healing dynamics of lung diseases such as COVID-19 or ARDS. The infected cells leave wounded regions in the epithelium which must be healed for the lung to return to a healthy state and carry out its main function of gas-exchange. Due to the complexity of the various interactions between cells of the lung epithelium and surrounding tissue, it is necessary to develop models that can complement experiments to fully understand the healing dynamics. In this mathematical study we model the mechanism of epithelial regeneration. We assume that healing is exclusively driven by progenitor cell proliferation, induced by a chemical activator such as epithelial growth factor (EGF) and cytokines such as interleukin-22 (IL22). Contrary to previous studies of wound healing, we consider the immune system, specifically the T effector cells TH1, TH17, TH22 and Treg to strongly contribute to the healing process, by producing IL22 or regulating the immune response. We therefore obtain a coupled system of two ordinary differential equations for the epithelial and immune cell densities and two functions for the levels of chemicals that either induce epithelial proliferation or recruit immune cells. These functions link the two cell equations. We find that to allow the epithelium to regenerate to a healthy state, the immune system must not exceed a threshold value at the onset of the healing phase. This immune threshold is supported experimentally but was not explicitly built into our equations. Our assumptions are therefore sufficient to reproduce experimental results concerning the ratio TH17/Treg cells as a threshold to predict higher mortality rates in patients. This immune threshold can be controlled by parameters of the model, specifically the base-level growth factor concentration. This conclusion is based on a mathematical bifurcation analysis and linearization of the model equations. Our results suggest treatment of severe cases of lung injury by reducing or suppressing the immune response, in an individual patient, assessed by their disease parameters such as course of lung injury and immune response levels.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

An agent-based modeling approach for lung fibrosis in response to COVID-19

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-β). The TGF-β-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-β sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-β, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-β, and the duration of TGF-β sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-β sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-β, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-β sources changed the fibrotic patterns; in the presence of persistent TGF-β sources, partial removal of TGF-β from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-β sources may find applications in the current clinical trials targeting TGF-β for the resolution of lung fibrosis.

Differences in Treatment Response in Bronchial Epithelial Cells from Idiopathic Pulmonary Fibrosis (IPF) Patients: A First Step towards Personalized Medicine?

Idiopathic pulmonary fibrosis (IPF) has a detrimental prognosis despite antifibrotic therapies to which individual responses vary. IPF pathology is associated with oxidative stress, inflammation and increased activation of SRC family kinases (SFK). This pilot study evaluates individual responses to pirfenidone, nintedanib and SFK inhibitor saracatinib, markers of redox homeostasis, fibrosis and inflammation, in IPF-derived human bronchial epithelial (HBE) cells. Differentiated HBE cells from patients with and without IPF were analyzed for potential alterations in redox and profibrotic genes and pro-inflammatory cytokine secretion. Additionally, the effects of pirfenidone, nintedanib and saracatinib on these markers were determined. HBE cells were differentiated into a bronchial epithelium containing ciliated epithelial, basal, goblet and club cells. NOX4 expression was increased in IPF-derived HBE cells but differed on an individual level. In patients with higher NOX4 expression, pirfenidone induced antioxidant gene expression. All drugs significantly decreased NOX4 expression. IL-6 (p = 0.09) and IL-8 secretion (p = 0.014) were increased in IPF-derived HBE cells and significantly reduced by saracatinib. Finally, saracatinib significantly decreased TGF-β gene expression. Our results indicate that treatment responsiveness varies between IPF patients in relation to their oxidative and inflammatory status. Interestingly, saracatinib tends to be more effective in IPF than standard antifibrotic drugs.

A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion

Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.

Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives

In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.

A 3D Agent-Based Model of Lung Fibrosis

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNβ production

MOTIVATION

The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNβ). Experimental evidence shows that IFNβ signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNβ production, an agent-based model is developed to simulate both DNA transfection and viral infection.

RESULTS

We show that heterogeneity can enhance IFNβ responses during infection. Furthermore, by varying the degree of IFNβ stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection.

AVAILABILITY AND IMPLEMENTATION

Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

Mycobacterium tuberculosis (Mtb) infection causes tuberculosis (TB), a disease characterized by development of granulomas. Granulomas consist of activated immune cells that cluster together to limit bacterial growth and restrict dissemination. Control of the TB epidemic has been limited by lengthy drug regimens, antibiotic resistance, and lack of a robustly efficacious vaccine. Fibrosis commonly occurs during treatment and is associated with both positive and negative disease outcomes in TB but little is known about the processes that initiate fibrosis in granulomas. Human and nonhuman primate granulomas undergoing fibrosis can have spindle-shaped macrophages with fibroblast-like morphologies suggesting a relationship between macrophages, fibroblasts, and granuloma fibrosis. This relationship has been difficult to investigate because of the limited availability of human pathology samples, the time scale involved in human TB, and overlap between fibroblast and myeloid cell markers in tissues. To better understand the origins of fibrosis in TB, we used a computational model of TB granuloma biology to identify factors that drive fibrosis over the course of local disease progression. We validated the model with granulomas from nonhuman primates to delineate myeloid cells and lung-resident fibroblasts. Our results suggest that peripheral granuloma fibrosis, which is commonly observed, can arise through macrophage-to-myofibroblast transformation (MMT). Further, we hypothesize that MMT is induced in M1 macrophages through a sequential combination of inflammatory and anti-inflammatory signaling in granuloma macrophages. We predict that MMT may be a mechanism underlying granuloma-associated fibrosis and warrants further investigation into myeloid cells as drivers of fibrotic disease.

Tuberculosis is a disease caused by Mycobacterium tuberculosis (Mtb), a bacterium that infects over a third of the world’s population. The only available vaccine for TB has limited efficacy and drug treatment involves several antibiotics that are taken for several months. These drugs can have significant side-effects and a lack of compliance can lead to drug resistance in Mtb. A hallmark of Mtb infection is the development of clusters of cells that form around infected macrophages to contain the infection called granulomas. Older granulomas, or granulomas in patients treated with antibiotic, often become fibrotic and this can cause chronic lung problems long after the Mtb infection has cleared. The process that drives a fibrotic outcome has been difficult to assess in vivo. Herein we combined wet-lab and computational experimentation to identify a novel mechanism leading to peripheral fibrosis in granulomas. We find that macrophages transforming into myofibroblast-like cells, may be a key pathway to granuloma-associated fibrosis, a phenomena that has not been well characterized in vivo. Further we identified factors that can inhibit fibrosis development during TB that could be therapeutic targets during TB treatment to limit the risk of long-term tissue damage in TB.

Type I Collagen Signaling Regulates Opposing Fibrotic Pathways through α2β1 Integrin

Fibrosis is characterized by fibroblast activation, leading to matrix remodeling culminating in a stiff, type I collagen-rich fibrotic matrix. Alveolar epithelial cell (AEC) apoptosis is also a major feature of fibrogenesis, and AEC apoptosis is sufficient to initiate a robust lung fibrotic response. TGF-β (transforming growth factor-β) is a major driver of fibrosis and can induce both AEC apoptosis and fibroblast activation. We and others have previously shown that changes in extracellular matrix stiffness and composition can regulate the cellular response to TGF-β. In the present study, we find that type I collagen signaling promotes TGF-β-mediated fibroblast activation and inhibits TGF-β-induced AEC death. Fibroblasts cultured on type I collagen or fibrotic decellularized lung matrix had augmented activation in response to TGF-β, whereas AECs on cultured on type I collagen or fibrotic lung matrix were more resistant to TGF-β-induced apoptosis. Both of these responses were mediated by integrin α₂β₁, a major collagen receptor. AECs treated with an α₂ integrin inhibitor or with deletion of α₂ integrin had loss of collagen-mediated protection from apoptosis. We found that mice with fibroblast-specific deletion of α₂ integrin were protected from fibrosis whereas mice with AEC-specific deletion of α₂ integrin had more lung injury and a greater fibrotic response to bleomycin. Intrapulmonary delivery of an α₂ integrin-activating collagen peptide inhibited AEC apoptosis in vitro and in vivo and attenuated the fibrotic response. These studies underscore the need for a thorough understanding of the divergent response to matrix signaling.

Diverse Injury Pathways Induce Alveolar Epithelial Cell CCL2/12, Which Promotes Lung Fibrosis

Accumulating evidence suggests that fibrosis is a multicellular process with contributions from alveolar epithelial cells (AECs), recruited monocytes/macrophages, and fibroblasts. We have previously shown that AEC injury is sufficient to induce fibrosis, but the precise mechanism remains unclear. Several cell types, including AECs, can produce CCL2 and CCL12, which can promote fibrosis through CCR2 activation. CCR2 signaling is critical for the initiation and progression of pulmonary fibrosis, in part through recruitment of profibrotic bone marrow-derived monocytes. Attempts at inhibiting CCL2 in patients with fibrosis demonstrated a marked upregulation of CCL2 production and no therapeutic response. To better understand the mechanisms involved in CCL2/CCR2 signaling, we generated mice with conditional deletion of CCL12, a murine homolog of human CCL2. Surprisingly, we found that mice with complete deletion of CCL12 had markedly increased concentrations of other CCR2 ligands and were not protected from fibrosis after bleomycin injury. In contrast, mice with lung epithelial cell-specific deletion of CCL12 were protected from bleomycin-induced fibrosis and had expression of CCL2 and CCL7 similar to that of control mice treated with bleomycin. Deletion of CCL12 within AECs led to decreased recruitment of exudate macrophages. Finally, injury to murine and human primary AECs resulted in increased production of CCL2 and CCL12, in part through activation of the mTOR pathway. In conclusion, these data suggest that targeting CCL2 may be a viable antifibrotic strategy once the pathways involved in the production and function of CCL2 and other CCR2 ligands are better defined.

Multi-scale models of lung fibrosis

The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.

In Vitro Granuloma Models of Tuberculosis: Potential and Challenges

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.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

Wound healing and fibrosis following myocardial infarction (MI) is a dynamic process involving many cell types, extracellular matrix (ECM), and inflammatory cues. As both incidence and survival rates for MI increase, management of post-MI recovery and associated complications are an increasingly important focus. Complexity of the wound healing process and the need for improved therapeutics necessitate a better understanding of the biochemical cues that drive fibrosis. To study the progression of cardiac fibrosis across spatial and temporal scales, we developed a novel hybrid multiscale model that couples a logic-based differential equation (LDE) model of the fibroblast intracellular signaling network with an agent-based model (ABM) of multi-cellular tissue remodeling. The ABM computes information about cytokine and growth factor levels in the environment including TGFβ, TNFα, IL-1β, and IL-6, which are passed as inputs to the LDE model. The LDE model then computes the network signaling state of individual cardiac fibroblasts within the ABM. Based on the current network state, fibroblasts make decisions regarding cytokine secretion and deposition and degradation of collagen. Simulated fibroblasts respond dynamically to rapidly changing extracellular environments and contribute to spatial heterogeneity in model predicted fibrosis, which is governed by many parameters including cell density, cell migration speeds, and cytokine levels. Verification tests confirmed that predictions of the coupled model and network model alone were consistent in response to constant cytokine inputs and furthermore, a subset of coupled model predictions were validated with in vitro experiments with human cardiac fibroblasts. This multiscale framework for cardiac fibrosis will allow for systematic screening of the effects of molecular perturbations in fibroblast signaling on tissue-scale extracellular matrix composition and organization.

Agent-based models of inflammation in translational systems biology: A decade later

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

A review of inflammatory mechanism in airway diseases

BACKGROUND

Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF).

FINDINGS

In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-β (TGF-β), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases.

CONCLUSION

The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.

Differential effects of Nintedanib and Pirfenidone on lung alveolar epithelial cell function in ex vivo murine and human lung tissue cultures of pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease. Repetitive injury and reprogramming of the lung epithelium are thought to be critical drivers of disease progression, contributing to fibroblast activation, extracellular matrix remodeling, and subsequently loss of lung architecture and function. To date, Pirfenidone and Nintedanib are the only approved drugs known to decelerate disease progression, however, if and how these drugs affect lung epithelial cell function, remains largely unexplored.

Methods

We treated murine and human 3D ex vivo lung tissue cultures (3D-LTCs; generated from precision cut lung slices (PCLS)) as well as primary murine alveolar epithelial type II (pmATII) cells with Pirfenidone or Nintedanib. Murine 3D-LTCs or pmATII cells were derived from the bleomycin model of fibrosis. Early fibrotic changes were induced in human 3D-LTCs by a mixture of profibrotic factors. Epithelial and mesenchymal cell function was determined by qPCR, Western blotting, Immunofluorescent staining, and ELISA.

Results

Low μM concentrations of Nintedanib (1 μM) and mM concentrations of Pirfenidone (2.5 mM) reduced fibrotic gene expression including Collagen 1a1 and Fibronectin in murine and human 3D-LTCs as well as pmATII cells. Notably, Nintedanib stabilized expression of distal lung epithelial cell markers, especially Surfactant Protein C in pmATII cells as well as in murine and human 3D-LTCs.

Conclusions

Pirfenidone and Nintedanib exhibit distinct effects on murine and human epithelial cells, which might contribute to their anti-fibrotic action. Human 3D-LTCs represent a valuable tool to assess anti-fibrotic mechanisms of potential drugs for the treatment of IPF patients.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0876-y) contains supplementary material, which is available to authorized users.

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

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

Understanding the relationship between cell death and tissue shrinkage via a stochastic agent-based model

Cell death, a process which can occur both naturally and in response to insult, is both a complex and diverse phenomenon. Under some circumstances, dying cells actively contract and cause their neighbors to rearrange and maintain tissue integrity. Under other circumstances, dying cells leave behind gaps, which results in tissue separation. A better understanding of how the cellular scale features of cell death manifest on the population scale has implications ranging from morphogenesis to tumor response to treatment. However, the mechanistic relationship between cell death and population scale shrinkage is not well understood, and computational methods for studying these relationships are not well established. Here we propose a mechanically robust agent-based cell model designed to capture the implications of cell death on the population scale. In our agent-based model, algorithmic rules applied on the cellular level emerge on the population scale where their effects are quantified. To better quantify model uncertainty and parameter interactions, we implement a recently developed technique for conducting a variance-based sensitivity analysis on the stochastic model. From this analysis and subsequent investigation, we find that cellular scale shrinkage has the largest influence of all model parameters tested, and that by adjusting cellular scale shrinkage population shrinkage varies widely even across simulations which contain the same fraction of dying cells. We anticipate that the methods and results presented here are a starting point for significant future investigation toward quantifying the implications of different mechanisms of cell death on population and tissue scale behavior.

Astragaloside IV Improves Bleomycin-Induced Pulmonary Fibrosis in Rats by Attenuating Extracellular Matrix Deposition

Pulmonary fibrosis is a devastating lung disorder with mysterious pathogenesis and limited treatment options. It is well-recognized that the uncontrolled proliferation of lung fibroblasts and differentiation of fibroblasts into myofibroblasts excessively produce extracellular matrix (ECM) proteins which contribute to the fibrosis change of the lungs. Thus, blocking ECM accumulation would delay fibrosis progression. In this study, we observed the effects of astragaloside IV (ASV) (10 mg/kg/d) on ECM proteins in bleomycin (BLM, 5 mg/kg)-treated rats. Our results showed that ASV not only ameliorated BLM-induced body weight loss, lung coefficient increase, histological changes and collagen secretion, but also reduced the levels of type III collagen (Col-III) in lung homogenate, laminin (LN) and hyaluronic acid (HA) in serum, as well as hydroxyproline (HYP) in lung tissue. Besides, ASV significantly down-regulated the levels of high-mobility group box1 (HMGB1) in serum and lung tissue, and inhibited the up-regulated expression of α-SMA (marker of myofibroblasts) in the lungs. Taken together, these findings indicate that ASV attenuates BLM-induced ECM deposition, supporting its use as a promising candidate to treat lung fibrosis.

Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a pulmonary pathogen of major global concern. A key feature of Mtb infection in primates is the formation of granulomas, dense cellular structures surrounding infected lung tissue. These structures serve as the main site of host-pathogen interaction in TB, and thus to effectively treat TB we must clarify mechanisms of granuloma formation and their function in disease. Fibrotic granulomas are associated with both good and bad disease outcomes. Fibrosis can serve to isolate infected tissue from healthy tissue, but it can also cause difficulty breathing as it leaves scars. Little is known about fibrosis in TB, and data from non-human primates is just beginning to clarify the picture. This work focuses on constructing a hybrid multi-scale model of fibrotic granuloma formation, in order to identify mechanisms driving development of fibrosis in Mtb infected lungs. We combine dynamics of molecular, cellular, and tissue scale models from previously published studies to characterize the formation of two common sub-types of fibrotic granulomas: peripherally fibrotic, with a cuff of collagen surrounding granulomas, and centrally fibrotic, with collagen throughout granulomas. Uncertainty and sensitivity analysis, along with large simulation sets, enable us to identify mechanisms differentiating centrally versus peripherally fibrotic granulomas. These findings suggest that heterogeneous cytokine environments exist within granulomas and may be responsible for driving tissue scale morphologies. Using this model we are primed to better understand the complex structure of granulomas, a necessity for developing successful treatments for TB.

Model-Driven Experimentation: A New Approach to Understand Mechanisms of Tertiary Lymphoid Tissue Formation, Function, and Therapeutic Resolution

The molecular and cellular processes driving the formation of secondary lymphoid tissues have been extensively studied using a combination of mouse knockouts, lineage-specific reporter mice, gene expression analysis, immunohistochemistry, and flow cytometry. However, the mechanisms driving the formation and function of tertiary lymphoid tissue (TLT) experimental techniques have proven to be more enigmatic and controversial due to differences between experimental models and human disease pathology. Systems-based approaches including data-driven biological network analysis (gene interaction network, metabolic pathway network, cell-cell signaling, and cascade networks) and mechanistic modeling afford a novel perspective from which to understand TLT formation and identify mechanisms that may lead to the resolution of tissue pathology. In this perspective, we make the case for applying model-driven experimentation using two case studies, which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.

Ginsenoside Rg1 Ameliorates Cigarette Smoke-Induced Airway Fibrosis by Suppressing the TGF-β1/Smad Pathway In Vivo and In Vitro

Small airway fibrosis is a key pathological process accompanying chronic obstructive pulmonary disease (COPD) and includes fibroblast/myofibroblast transdifferentiation and excessive extracellular matrix deposition. Ginsenoside Rg1, one of the main active ingredients of Panax ginseng, has been shown to exert an antifibrotic effect in many tissues. However, little is known about the underlying mechanism and whether ginsenoside Rg1 can exert an effect on small airway fibrosis. We investigated the anti-small airway fibrosis effects of ginsenoside Rg1 in human embryonic lung fibroblasts and in COPD rats. We found that ginsenoside Rg1 effectively reduced the degree of pulmonary fibrosis, decreased the expression of α-smooth muscle actin, collagen I, and matrix metalloproteinase 9, and maintained the ratio of matrix metalloproteinase 9 to tissue inhibitor of metalloproteinase 1. Importantly, ginsenoside Rg1 significantly attenuated cigarette smoke extract-induced upregulation of transforming growth factor β1, TGF-β receptor I, phospho-Smad2, and phospho-Smad3. In addition, ginsenoside Rg1 mimicked the effect of SB525334, a TGF-β receptor I-Smad2/3 inhibitor. Collectively, these results suggest that ginsenoside Rg1 may suppress cigarette smoke-induced airway fibrosis in pulmonary fibroblasts and COPD rats by inhibiting the TGF-β1/Smad signaling pathway.

Lung epithelial cell focal adhesion kinase signaling inhibits lung injury and fibrosis

Progressive pulmonary fibrosis is a devastating consequence of many acute and chronic insults to the lung. Lung injury leads to alveolar epithelial cell (AEC) death, destruction of the basement membrane, and activation of transforming growth factor-β (TGF-β). There is subsequent resolution of the injury and a coordinated and concurrent initiation of fibrosis. Both of these processes may involve activation of similar intracellular signaling pathways regulated in part by dynamic changes to the extracellular matrix. Matrix signaling can augment the profibrotic fibroblast response to TGF-β. However, similar matrix/integrin signaling pathways may also be involved in the inhibition of ongoing TGF-β-induced AEC apoptosis. Focal adhesion kinase (FAK) is an integrin-associated signaling molecule expressed by many cell types. We used mice with AEC-specific FAK deletion to isolate the epithelial aspect of integrin signaling in the bleomycin model of lung injury and fibrosis. Mice with AEC-specific deletion of FAK did not exhibit spontaneous lung injury but did have significantly greater terminal deoxynucleotidyl transferase dUTP-mediated nick-end labeling-positive cells (18.6 vs. 7.1) per ×200 field, greater bronchoalveolar lavage protein (3.2 vs. 1.8 mg/ml), and significantly greater death (77 vs. 19%) after bleomycin injury compared with littermate control mice. Within primary AECs, activated FAK directly associates with caspase-8 and inhibits activation of the caspase cascade resulting in less apoptosis in response to TGF-β. Our studies support a model in which dynamic changes to the extracellular matrix after injury promote fibroblast activation and inhibition of epithelial cell apoptosis in response to TGF-β through FAK activation potentially complicating attempts to nonspecifically target this pathway for antifibrotic therapy.

Cell and tissue-based in vitro models for improving the development of oral inhalation drug products

The interplay of costs and ethics has increased the need for adequate models mimicking the human in vivo situation drastically. An adequate model has the ability to generate data which are predictive for a certain aspect of the human response, for example for the bioavailability. This review highlights how in vitro models can enrich pulmonary drug delivery research with more detailed insights in cellular and non-cellular barriers, allowing for faster improvements and significant innovations of inhalation drug products. Risk assessment in inhalation toxicology and aerosol medicines and related important guidelines (e.g. OECD, EMA) are mentioned as a fundament for the described methods. Principle decisions to find a suitable in vitro tool for the question being asked are discussed to support the individual selection. Depending on the cellular and non-cellular barrier, exemplary in vitro tools are described with their ability to reflect a certain part of the in vivo lung situation. The review closes with a short summary of more complex systems as well as their advantages and limitations.