A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology

Protocol for differentiation of functional macrophages from human induced pluripotent stem cells

Human induced pluripotent stem cell (hiPSC)-derived macrophages provide a valuable tool for disease modeling and drug discovery. Here, we present a protocol to generate functional macrophages from hiPSCs using a feeder-free hematopoietic differentiation technique. We describe steps for preparing hiPSCs, mesodermal differentiation, hematopoietic commitment, and macrophage differentiation and expansion. We then detail assays to characterize their phenotype, polarization, and phagocytic functions. The functional macrophages generated here could be used to generate organoids for disease modeling and drug discovery studies. For complete details on the use and execution of this protocol, please refer to Jeong et al.1 and Heo et al.2.

Suppression of SOCS3 expression in macrophage cells: Potential application in diabetic wound healing

Impaired polarization of M1 to M2 macrophages has been reported in diabetic wounds. We aimed to improve this polarization by down-regulation of expression of the "Suppressor of Cytokine Signaling 3" (SOCS3) gene in macrophages. Two oligodeoxynucleotide (ASO) sequences were designed against SOC3 mRNA and were loaded to mannosylated-polyethyleneimine (Man-PEI). The optimum N/P ratio for Man-PEI-ASO was determined to be 8 based on loading efficiency, particle size, zeta potential, cellular uptake and cytotoxicity assay. pH stability of ASO in Man-PEI-ASO and its protection from DNase I was confirmed. After in vitro treatment of macrophages with Man-PEI-ASO, SOCS3 was downregulated, SOCS1 upregulated, and SOCS1/SOCS3 ratio increased. Also, expressions of macrophage markers of M2 (IL-10, Arg1, CD206) increased and those of M1 (IL-1β, NOS2, CD68) decreased, and secretion of pro-inflammatory cytokines (TNF-α and IL-1β) decreased while that of anti-inflammatory cytokine IL-4 increased. All suggested a polarization into M2 phenotype. Finally, the Man-PEI-ASO was loaded in hydrogel and applied to a diabetic wound model in mice. It improved the healing to the level observed in non-diabetic wounds. We show that using antisense sequences against SOC3 mRNA, macrophage polarization could be directed into the M2 phenotype and healing of diabetic wound could be highly improved.

Agent-based vs. equation-based multi-scale modeling for macrophage polarization

Macrophages show high plasticity and result in heterogenic subpopulations or polarized states identified by specific cellular markers. These immune cells are typically characterized as pro-inflammatory, or classically activated M1, and anti-inflammatory, or alternatively activated M2. However, a more precise definition places them along a spectrum of activation where they may exhibit a number of pro- or anti-inflammatory roles. To understand M1-M2 dynamics in the context of a localized response and explore the results of different mathematical modeling approaches based on the same biology, we utilized two different modeling techniques, ordinary differential equation (ODE) modeling and agent-based modeling (ABM), to simulate the spectrum of macrophage activation to general pro- and anti-inflammatory stimuli on an individual and multi-cell level. The ODE model includes two hallmark pro- and anti-inflammatory signaling pathways and the ABM incorporates similar M1-M2 dynamics but in a spatio-temporal platform. Both models link molecular signaling with cellular-level dynamics. We then performed simulations with various initial conditions to replicate different experimental setups. Similar results were observed in both models after tuning to a common calibrating experiment. Comparing the two models' results sheds light on the important features of each modeling approach. When more data is available these features can be considered when choosing techniques to best fit the needs of the modeler and application.

Reframing macrophage diversity with network motifs

A binary classification of macrophage activation as inflammatory or resolving does not capture the diversity of macrophage states observed in tissues. However, framing macrophage activation as a continuous spectrum of states overlooks the intracellular and extracellular networks that regulate and coordinate macrophage responses. Here, we suggest that the systems biology concept of network motifs, which incorporate rules of local molecular interactions, is useful for reframing macrophage activation. Because network motifs can be used to regulate distinct biological functions, they offer a simplified unit that can be compared across organismal, tissue, and disease contexts. Moreover, defining macrophage states as combinations of functional modules regulated by network motifs offers a framework to ultimately predict and target macrophage responses arising in complex environments.

Macrophage phenotype transitions in a stochastic gene-regulatory network model

Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.

Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach

The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.

Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway

Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between normoxia and hypoxia can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. We calibrate and fit the model parameters based on well-established modeling techniques. Our results indicate that the main pathways involved in the patterning of tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia affects how a reaction affects patterning. Interestingly, the interaction of receptors with Neuropilin1 is also relevant for cell patterning. Our simulations under different oxygen concentrations indicate time- and oxygen-availability-dependent responses for the two cells. Following simulations with various stimuli, our model suggests that factors such as period under hypoxia and oxygen availability must be considered for pattern control. This project provides insights into the signaling and patterning of endothelial cells under hypoxia, contributing to studies in the field.

The osteogenetic activities of mesenchymal stem cells in response to Mg2+ ions and inflammatory cytokines: a numerical approach using fuzzy logic controllers

Magnesium (Mg²⁺) ions are frequently reported to regulate osteogenic activities of mesenchymal stem cells (MSCs). In this study, we propose a numerical model to study the regulatory importance of Mg²⁺ ions on MSCs osteoblastic differentiation in the presence of an inflammatory response. A fuzzy logic controller was formulated to receive the concentrations of Mg²⁺ ions and the inflammatory cytokines of TNF-α, IL-10, IL-1β, and IL-8 as cellular inputs and predict the cells’ early and late differentiation rates. Five sets of empirical data obtained from published cell culture experiments were used to calibrate the model. The model successfully reproduced the empirical data regarding the concentration- and phase-dependent effect of Mg²⁺ ions on the differentiation process. In agreement with the experiments, the model showed the stimulatory role of Mg²⁺ ions on the early differentiation phase, once administered at low concentration, and their inhibitory role on the late differentiation phase. The numerical approach used in this study suggested 6–8 mM as the most effective concentration of Mg²⁺ ions in promoting the early differentiation process. Also, the proposed model sheds light on the fundamental differences in the behavioral properties of cells cultured in different experiments, e.g. differentiation rate and the sensitivity of the cultured cells to stimulatory signals such as Mg²⁺ ions. Thus, it can be used to interpret and compare different empirical findings. Moreover, the model successfully reproduced the nonlinearities in the concentration-dependent role of the inflammatory cytokines in early and late differentiation rates. Overall, the proposed model can be employed in studying the osteogenic properties of Mg-based implants in the presence of an inflammatory response.

Magnesium (Mg) is an attractive material for bone implants as it fully degrades after implantation, saving pain and cost of the second surgery for implant removal. To advance its application in the orthopedic industry, it is paramount to fully understand the biological impact of the degradation products, in particular Mg²⁺ ions. Here, we propose a computer model to study the effects of Mg²⁺ ions on bone regeneration. The model focuses on stem cells and includes both the direct stimulation effects of Mg²⁺ ions on cells and the indirect stimulus through the inflammatory system. The proposed model successfully reproduced the experimental data of five different studies. The model additionally highlighted differences amongst different experiments in terms of the cellular response to Mg²⁺ ions. The proposed system therefore provides an important addition to the field of Mg implant research.

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

•

A mechanistic model of quantitative systems pharmacology in immuno-oncology

•

Dynamics of tumor-associated macrophages are integrated into our previous work

•

Conducting in silico clinical trials to predict clinical response to cancer therapy

Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in aspergillosis, we generated a transcriptomic time series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2 to 4 h after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of transferrin receptor-1 (TfR1) after fungal recognition, independent of the iron regulatory protein-labile iron pool (IRP-LIP) system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis.

IMPORTANCE Invasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

Interferon-γ promotes monocyte-mediated lung injury during influenza infection

Influenza A virus (IAV) infection triggers an exuberant host response that promotes acute lung injury. However, the host response factors that promote the development of a pathologic inflammatory response to IAV remain incompletely understood. In this study, we identify an interferon-γ (IFN-γ)-regulated subset of monocytes, CCR2⁺ monocytes, as a driver of lung damage during IAV infection. IFN-γ regulates the recruitment and inflammatory phenotype of CCR2⁺ monocytes, and mice deficient in CCR2 (CCR2^-/-) or IFN-γ (IFN-γ^-/-) exhibit reduced lung inflammation, pathology, and disease severity. Adoptive transfer of wild-type (WT) (IFN-γR1^+/+) but not IFN-γR1^-/- CCR2⁺ monocytes restore the WT-like pathological phenotype of lung damage in IAV-infected CCR2^-/- mice. CD8⁺ T cells are the main source of IFN-γ in IAV-infected lungs. Collectively, our data highlight the requirement of IFN-γ signaling in the regulation of CCR2⁺ monocyte-mediated lung pathology during IAV infection.

Multistability in Macrophage Activation Pathways and Metabolic Implications

Macrophages are innate immune cells with a dynamic range of reversible activation states including the classical pro-inflammatory (M1) and alternative anti-inflammatory (M2) states. Deciphering how macrophages regulate their transition from one state to the other is key for a deeper understanding of inflammatory diseases and relevant therapies. Common regulatory motifs reported for macrophage transitions, such as positive or double-negative feedback loops, exhibit a switchlike behavior, suggesting the bistability of the system. In this review, we explore the evidence for multistability (including bistability) in macrophage activation pathways at four molecular levels. First, a decision-making module in signal transduction includes mutual inhibitory interactions between M1 (STAT1, NF-KB/p50-p65) and M2 (STAT3, NF-KB/p50-p50) signaling pathways. Second, a switchlike behavior at the gene expression level includes complex network motifs of transcription factors and miRNAs. Third, these changes impact metabolic gene expression, leading to switches in energy production, NADPH and ROS production, TCA cycle functionality, biosynthesis, and nitrogen metabolism. Fourth, metabolic changes are monitored by metabolic sensors coupled to AMPK and mTOR activity to provide stability by maintaining signals promoting M1 or M2 activation. In conclusion, we identify bistability hubs as promising therapeutic targets for reverting or blocking macrophage transitions through modulation of the metabolic environment.

Dynamic Multiscale Regulation of Perfusion Recovery in Experimental Peripheral Arterial Disease: A Mechanistic Computational Model

•

A first-of-a-kind systems biology computational model is presented that describes multiscale regulation of perfusion recovery in experimental peripheral arterial disease.

•

Multilevel model calibration and validation enable high-resolution model simulations for experimental peripheral arterial disease (mouse HLI).

•

An integrative model-based mechanistic characterization of the intracellular, cellular, and tissue-level features critical for the dynamic reconstitution of perfusion following different patterns of occlusion-induced ischemia in HLI is described.

•

Using a model-based virtual HLI mouse population, pharmacologic inhibition of cell necrosis is predicted as a strategy with high therapeutic potential to improve perfusion recovery; in real HLI mice, the positive impact of this new strategy is then experimentally studied and confirmed.

In peripheral arterial disease (PAD), the degree of endogenous capacity to modulate revascularization of limb muscle is central to the management of leg ischemia. To characterize the multiscale and multicellular nature of revascularization in PAD, we have developed the first computational systems biology model that mechanistically incorporates intracellular, cellular, and tissue-level features critical for the dynamic reconstitution of perfusion after occlusion-induced ischemia. The computational model was specifically formulated for a preclinical animal model of PAD (mouse hindlimb ischemia [HLI]), and it has gone through multilevel model calibration and validation against a comprehensive set of experimental data so that it accurately captures the complex cellular signaling, cell–cell communication, and function during post-HLI perfusion recovery. As an example, our model simulations generated a highly detailed description of the time-dependent spectrum-like macrophage phenotypes in HLI, and through model sensitivity analysis we identified key cellular processes with potential therapeutic significance in the pathophysiology of PAD. Furthermore, we computationally evaluated the in vivo effects of different targeted interventions on post-HLI tissue perfusion recovery in a model-based, data-driven, virtual mouse population and experimentally confirmed the therapeutic effect of a novel model-predicted intervention in real HLI mice. This novel multiscale model opens up a new avenue to use integrative systems biology modeling to facilitate translational research in PAD.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Cholinergic Elicitation Prevents Ventricular Remodeling via Alleviations of Myocardial Mitochondrial Injury Linked to Inflammation in Ischemia-Induced Chronic Heart Failure Rats

Background

Cholinergic anti-inflammatory pathway (CAP) is implicated in cardioprotection in chronic heart failure (CHF) by downregulating inflammation response. Mitochondrial injuries play an important role in ventricular remodeling of the CHF process. Herein, we aim to investigate whether CAP elicitation prevents ventricular remodeling in CHF by protecting myocardial mitochondrial injuries and its underlying mechanisms.

Methods and Results

CHF models were established by ligation of anterior descending artery for 5 weeks. Postoperative survival rats were assigned into 5 groups: the sham group (sham, n = 10), CHF group (CHF, n = 11), Vag group (CHF+vagotomy, n = 10), PNU group (CHF+PNU-282987 for 4 weeks, n = 11), and Vag+PNU group (CHF+vagotomy+PNU-282987 for 4 weeks, n = 10). The antiventricular remodeling effect of cholinergic elicitation was evaluated in vivo, and H9C2 cells were selected for the TNF-α gradient stimulation experiment in vitro. In vivo, CAP agitated by PNU-282987 alleviated the left ventricular dysfunction and inhibited the energy metabolism remodeling. Further, cholinergic elicitation increased myocardium ATP levels and reduced systemic inflammation. CAP induction alleviates macrophage infiltration and cardiac fibrosis, of which the effect is counteracted by vagotomy. Myocardial mitochondrial injuries were ameliorated by CAP activation, including the reserved ultrastructural integrity, declining ROS overload, reduced myocardial apoptosis, and enhanced mitochondrial fusion. In vitro, TNF-α intervention significantly exacerbated the mitochondrial damage in H9C2 cells.

Conclusion

CAP elicitation effectively improves ischemic ventricular remodeling by suppressing systemic and cardiac inflammatory response, attenuating cardiac fibrosis and potentially alleviating the mitochondrial dysfunction linked to hyperinflammation reaction.

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model

The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a systems-level characterization of macrophage signaling and activation from quantitative, temporal, dose-dependent, and single-cell aspects. This protocol includes instructions to utilize this model to computationally explore different biological scenarios with high resolution and efficiency. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2021).

Macrophage polarization-associated lnc-Ma301 interacts with caprin-1 to inhibit hepatocellular carcinoma metastasis through the Akt/Erk1 pathway

Background

Epithelial–mesenchymal transition (EMT) promotes migration, invasion, and metastasis of hepatocellular carcinoma (HCC) cells. The molecular mechanisms behind EMT and metastasis in HCC remain unclear.

Methods

Microarray analysis was used to identify lncRNAs expression during polarization of U937 macrophages from M2 to M1 phenotype. The expression of the identified lncRNA was compared between clinical samples of HCC tissues or adjacent normal tissues, as well as between HCC and normal liver cell lines. lnc-Ma301 was overexpressed or knocked-down in HCC cell lines, and the effects were assessed in vitro and in vivo. Interactions among lnc-Ma301 and its potential downstream targets caprin-1 were investigated in HCC cell lines. Effects of lnc-Ma301 over- and underexpression on the Akt/Erk1 signaling pathways were examined.

Results

Microarray analyses identified lnc-Ma301 as one of the most overexpressed long non-coding RNAs during polarization of U937 macrophages from M2 to M1 phenotype. Lnc-Ma301 showed lower expression in HCC tissues than in adjacent normal tissues, and lower expression was associated with worse prognosis. Activation of lnc-Ma301 inhibited cell proliferation, migration and EMT in HCC cell cultures, and it inhibited lung metastasis of HCC tumors in mice. Mechanistic studies suggested that lnc-Ma301 interacts with caprin-1 to inhibit HCC metastasis and EMT through Akt/Erk1 pathway.

Conclusions

Lnc-Ma301 may help regulate onset and metastasis of HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02133-1.

Reparative and Maladaptive Inflammation in Tendon Healing

Tendon injuries are common and debilitating, with non-regenerative healing often resulting in chronic disease. While there has been considerable progress in identifying the cellular and molecular regulators of tendon healing, the role of inflammation in tendon healing is less well understood. While inflammation underlies chronic tendinopathy, it also aids debris clearance and signals tissue repair. Here, we highlight recent findings in this area, focusing on the cells and cytokines involved in reparative inflammation. We also discuss findings from other model systems when research in tendon is minimal, and explore recent studies in the treatment of human tendinopathy to glean further insights into the immunobiology of tendon healing.

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Mesenchymal stem cells (MSCs) are proliferative and multipotent cells that play a key role in the bone regeneration process. Empirical data have repeatedly shown the bioregulatory importance of magnesium (Mg) ions in MSC growth and osteogenesis. In this study, we propose an agent-based model to predict the spatiotemporal dynamics of the MSC population and osteogenic differentiation in response to Mg2+ ions. A fuzzy-logic controller was designed to govern the decision-making process of cells by predicting four cellular processes of proliferation, differentiation, migration, and mortality in response to several important bioregulatory factors such as Mg2+ ions, pH, BMP2, and TGF-β1. The model was calibrated using the empirical data obtained from three sets of cell culture experiments. The model successfully reproduced the empirical observations regarding live cell count, viability, DNA content, and the differentiation-related markers of alkaline phosphate (ALP) and osteocalcin (OC). The simulation results, in agreement with the empirical data, showed that Mg2+ ions within 3-6 mM concentration have the highest stimulation effect on cell population growth. The model also correctly reproduced the stimulatory effect of Mg2+ ions on ALP and its inhibitory effect on OC as the early and late differentiation markers, respectively. Besides, the numerical simulation shed light on the innate cellular differences of the cells cultured in different experiments in terms of the proliferative capacity as well as sensitivity to Mg2+ ions. The proposed model can be adopted in the study of the osteogenesis around Mg-based implants where ions released due to degradation interact with local cells and regulate bone regeneration.

Mathematical modeling of ventilator-induced lung inflammation

Despite the benefits of mechanical ventilators, prolonged or misuse of ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we developed a mathematical model of interactions between the immune system and site of damage while accounting for macrophage phenotype. Through Latin hypercube sampling we generated a collection of parameter sets that are associated with a numerical steady state. We then simulated ventilation-induced damage using these steady state values as the initial conditions in order to evaluate how baseline immune state and lung health affect outcomes. We used a variety of methods to analyze the resulting parameter sets, transients, and outcomes, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation are important factors. Using the results of this analysis, we hypothesized interventions and used these treatment strategies to modulate the response to ventilation for particular parameters sets.

A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.

Multi-scale modeling of macrophage-T cell interactions within the tumor microenvironment

Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune-promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.

Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization

In this paper, we present and analyze a mathematical model for polarization of a single macrophage which, despite its simplicity, exhibits complex dynamics in terms of multistability. In particular, we demonstrate that an asymmetry in the regulatory mechanisms and parameter values is important for observing multiple phenotypes. Bifurcation and sensitivity analyses show that external signaling cues are necessary for macrophage commitment and emergence to a phenotype, but that the intrinsic macrophage pathways are equally important. Based on our numerical results, we formulate hypotheses that could be further investigated by laboratory experiments to deepen our understanding of macrophage polarization.

Mediation of Cardiac Macrophage Activity via Auricular Vagal Nerve Stimulation Ameliorates Cardiac Ischemia/Reperfusion Injury

Background

Myocardial infarction (MI) reperfusion therapy causes paradoxical cardiac complications. Following restoration of blood flow to infarcted regions, a multitude of inflammatory cells are recruited to the site of injury for tissue repair. Continual progression of cardiac inflammatory responses does, however, lead to adverse cardiac remodeling, inevitably causing heart failure.

Main Body

Increasing evidence of the cardioprotective effects of both invasive and non-invasive vagal nerve stimulation (VNS) suggests that these may be feasible methods to treat myocardial ischemia/reperfusion injury via anti-inflammatory regulation. The mechanisms through which auricular VNS controls inflammation are yet to be explored. In this review, we discuss the potential of autonomic nervous system modulation, particularly via the parasympathetic branch, in ameliorating MI. Novel insights are provided about the activation of the cholinergic anti-inflammatory pathway on cardiac macrophages. Acetylcholine binding to the α7 nicotinic acetylcholine receptor (α7nAChR) expressed on macrophages polarizes the pro-inflammatory into anti-inflammatory subtypes. Activation of the α7nAChR stimulates the signal transducer and activator of transcription 3 (STAT3) signaling pathway. This inhibits the secretion of pro-inflammatory cytokines, limiting ischemic injury in the myocardium and initiating efficient reparative mechanisms. We highlight recent developments in the controversial auricular vagal neuro-circuitry and how they may relate to activation of the cholinergic anti-inflammatory pathway.

Conclusion

Emerging published data suggest that auricular VNS is an inexpensive healthcare modality, mediating the dynamic balance between pro- and anti-inflammatory responses in cardiac macrophages and ameliorating cardiac ischemia/reperfusion injury.

Adipose tissue-derived stromal cells stimulated macrophages-endothelial cells interactions promote effective ischemic muscle neovascularization

Neovascularization, the process of new blood vessels formation in response to hypoxia induced signals, is an essential step during wound healing or ischemia repair. It follows as a cascade of consecutive events leading to new blood vessels formation and their subsequent remodeling to a mature and functional state, enabling tissue regeneration. Any disruption in consecutive stages of neovascularization can lead to chronic wounds or impairment of tissue repair. In the study we try to explain the biological basis of accelerated blood vessels formation in ischemic tissue after adipose tissue-derived stromal cells (ADSCs) administration. Experiments were performed on mouse models of hindlimb ischemia. We have evaluated the level of immune cells (neutrophils, macrophages) infiltration. The novelty of our work was the assessment of bone marrow-derived stem/progenitor cells (BMDCs) infiltration and their contribution to the neovascularization process in ischemic tissue. We have noticed that ADSCs regulated immune response and affected the kinetics and ratio of macrophages population infiltrating ischemic tissue. Our research revealed that ADSCs promoted changes in the morphology of infiltrating macrophages and their tight association with forming blood vessels. We assume that recruited macrophages may take over the role of pericytes and stabilize the new blood vessel or even differentiate into endothelial cells, which in consequence can accelerate vascular formation upon ADSCs administration. Our findings indicate that administration of ADSCs into ischemic muscle influence spatio-temporal distribution of infiltrating cells (macrophages, neutrophils and BMDCs), which are involved in each step of vascular formation, promoting effective ischemic tissue neovascularization.

A Quantitative Systems Pharmacology Model of T Cell Engager Applied to Solid Tumor

Cancer immunotherapy has recently drawn remarkable attention as promising results in the clinic have shown its ability to improve the overall survival, and T cells are considered to be one of the primary effectors for cancer immunotherapy. Enhanced and restored T cell tumoricidal activity has shown great potential for killing cancer cells. Bispecific T cell engagers (TCEs) are a growing class of molecules that are designed to bind two different antigens on the surface of T cells and cancer cells to bring them in close proximity and selectively activate effector T cells to kill target cancer cells. New T cell engagers are being investigated for the treatment of solid tumors. The activity of newly developed T cell engagers showed a strong correlation with tumor target antigen expression. However, the correlation between tumor-associated antigen expression and overall response of cancer patients is poorly understood. In this study, we used a well-calibrated quantitative systems pharmacology (QSP) model extended to bispecific T cell engagers to explore their efficacy and identify potential biomarkers. In principle, patient-specific response can be predicted through this model according to each patient's individual characteristics. This extended QSP model has been calibrated with available experimental data and provides predictions of patients' response to TCE treatment.

Regulation of the tumor immune microenvironment and vascular normalization in TNBC murine models by a novel peptide

Triple-negative breast cancer (TNBC) is a highly metastatic and aggressive disease with limited treatment options. Recently, the combination of the immune checkpoint inhibitor (ICI) atezolizumab (anti-PD-L1) with nab-paclitaxel was approved following a clinical trial that showed response rates in at least 43% of patients. While this approval marks a major advance in the treatment of TNBC it may be possible to improve the efficacy of ICI therapies through further modulation of the suppressive tumor immune microenvironment (TIME). Several factors may limit immune response in TNBC including aberrant growth factor signaling, such as VEGFR2 and cMet signaling, inefficient vascularization, poor delivery of drugs and immune cells, and the skewing of immune cell populations toward immunosuppressive phenotypes. Here we investigate the immune-modulating properties of AXT201, a novel 20 amino-acid integrin-binding peptide in two syngeneic mouse TNBC models: 4T1-BALB/c and NT4-FVB. AXT201 treatment improved survival in the NT4 model by 20% and inhibited the growth of 4T1 tumors by 47% over 22 days post-inoculation. Subsequent immunohistochemical analyses of 4T1 tumors also showed a 53% reduction in vascular density and a 184% increase in pericyte coverage following peptide treatment. Flow cytometry analyses demonstrated evidence of a more favorable anti-tumor immune microenvironment following treatment with AXT201, including significant decreases in the populations of T regulatory cells, monocytic myeloid-derived suppressor cells, and PD-L1 expressing cells and increased expression of T cell functional markers. Together, these findings demonstrate immune-activating properties of AXT201 that could be developed in combination with other immunomodulatory agents in the treatment of TNBC.

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.