Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Logic-based modeling of biological networks with Netflux

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux. Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Multi-Scale Multi-Cell Computational Model of Inflammation-Mediated Aortic Remodeling in Hypertension

PURPOSE

Multiple cell types interact within the aortic wall to control development, homeostasis, and adaptation as well as to drive disease progression. Given the complexity of these interactions and their manifestations at the tissue level, there is a pressing need for a new class of computational models that integrate data across scales.

METHODS

We meld logic-based cell signaling models of vascular smooth muscle cells, adventitial fibroblasts, and macrophages and couple this multi-cell model with a tissue level-constrained mixture model of aortic growth and remodeling. The coupled multi-scale model is parameterized using data from the literature and then specialized for the case of angiotensin II-induced hypertensive remodeling of the descending thoracic aorta in wild-type mice.

RESULTS

We contrast important contributions of chemo- and mechano-stimulation of cell responses and identify critical roles of recruited macrophages in driving the non-homeostatic thickening of the adventitial layer that reduces biaxial wall stress below setpoint values.

CONCLUSION

We show the utility of a multi-scale, multi-cell model in delineating effects of different chemo-mechanical stimuli in aortic remodeling in hypertension.

Linking signal input, cell state, and spatial context to inflammatory responses

Signal integration is central to a causal understanding of appropriately scaled inflammatory responses. Here, we discuss recent progress in our understanding of the stimulus-response linkages downstream of pro-inflammatory inputs, with special attention to (1) the impact of cell state on the specificity of evoked gene expression and (2) the critical role of the spatial context of stimulus exposure. Advances in these directions are emerging from new tools for inferring cell-cell interactions and the activities of cytokines and transcription factors in complex microenvironments, enabling analysis of signal integration in tissue settings. Building on data-driven elucidation of factors driving inflammatory outcomes, mechanistic modeling can then contribute to a quantitative understanding of regulatory events that balance protective versus pathological inflammation.

Logic-based modeling of biological networks with Netflux

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Cell signaling and tissue remodeling in the pulmonary autograft after the Ross procedure: A computational study

In the Ross procedure, a patient's pulmonary valve is transplanted in the aortic position. Despite advantages of this surgery, reoperation is still needed in many cases due to excessive dilatation of the pulmonary autograft. To further understand the failure mechanisms, we propose a multiscale model predicting adaptive processes in the autograft at the cell and tissue scale. The cell-scale model consists of a network model, that includes important signaling pathways and relations between relevant transcription factors and their target genes. The resulting gene activity leads to changes in the mechanical properties of the tissue, modeled as a constrained mixture of collagen, elastin and smooth muscle. The multiscale model is calibrated with findings from experiments in which seven sheep underwent the Ross procedure. The model is then validated against a different set of sheep experiments, for which a qualitative agreement between model and experiment is found. Model outcomes at the cell scale, including the activity of genes and transcription factors, also match experimentally obtained transcriptomics data.

Getting everyone to agree on gene signatures for murine macrophage polarization in vitro

Macrophages, key players in the innate immune system, showcase remarkable adaptability. Derived from monocytes, these phagocytic cells excel in engulfing and digesting pathogens and foreign substances as well as contributing to antigen presentation, initiating and regulating adaptive immunity. Macrophages are highly plastic, and the microenvironment can shaper their phenotype leading to numerous distinct polarized subsets, exemplified by the two ends of the spectrum: M1 (classical activation, inflammatory) and M2 (alternative activation, anti-inflammatory). RNA sequencing (RNA-Seq) has revolutionized molecular biology, offering a comprehensive view of transcriptomes. Unlike microarrays, RNA-Seq detects known and novel transcripts, alternative splicing, and rare transcripts, providing a deeper understanding of genome complexity. Despite the decreasing costs of RNA-Seq, data consolidation remains limited, hindering noise reduction and the identification of authentic signatures. Macrophages polarization is routinely ascertained by qPCR to evaluate those genes known to be characteristic of M1 or M2 skewing. Yet, the choice of these genes is literature- and experience-based, lacking therefore a systematic approach. This manuscript builds on the significant increase in deposited RNA-Seq datasets to determine an unbiased and robust murine M1 and M2 polarization profile. We now provide a consolidated list of global M1 differentially expressed genes (i.e. robustly modulated by IFN-γ, LPS, and LPS+ IFN-γ) as well as consolidated lists of genes modulated by each stimulus (IFN-γ, LPS, LPS+ IFN-γ, and IL-4).

Fatty acid metabolism of immune cells: a new target of tumour immunotherapy

Metabolic competition between tumour cells and immune cells for limited nutrients is an important feature of the tumour microenvironment (TME) and is closely related to the outcome of tumour immune escape. A large number of studies have proven that tumour cells need metabolic reprogramming to cope with acidification and hypoxia in the TME while increasing energy uptake to support their survival. Among them, synthesis, oxidation and uptake of fatty acids (FAs) in the TME are important manifestations of lipid metabolic adaptation. Although different immune cell subsets often show different metabolic characteristics, various immune cell functions are closely related to fatty acids, including providing energy, providing synthetic materials and transmitting signals. In the face of the current situation of poor therapeutic effects of tumour immunotherapy, combined application of targeted immune cell fatty acid metabolism seems to have good therapeutic potential, which is blocked at immune checkpoints. Combined application of adoptive cell therapy and cancer vaccines is reflected. Therefore, it is of great interest to explore the role of fatty acid metabolism in immune cells to discover new strategies for tumour immunotherapy and improve anti-tumour immunity.

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.

Experiment-based computational model predicts that IL-6 classic and trans-signaling exhibit similar potency in inducing downstream signaling in endothelial cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as cardiovascular diseases and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, and muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and ERK (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 classic and trans-signaling showed similar potency in inducing downstream signaling; however, trans-signaling induces stronger downstream responses and plays a dominant role in the overall effects from IL-6 due to the in vitro experimental setting of abundant sIL-6R. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the downstream inflammatory and/or angiogenic signals, pSTAT3, pAkt, and pERK responses. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify potential targets that influence IL-6 mediated signaling in endothelial cells and to study their effects quantitatively in modulating STAT3, Akt, and ERK activation.

A computational bridge between traction force microscopy and tissue contraction

Arterial wall active mechanics are driven by resident smooth muscle cells, which respond to biological, chemical, and mechanical stimuli and activate their cytoskeletal machinery to generate contractile stresses. The cellular mechanoresponse is sensitive to environmental perturbations, often leading to maladaptation and disease progression. When investigated at the single cell scale, however, these perturbations do not consistently result in phenotypes observed at the tissue scale. Here, a multiscale model is introduced that translates microscale contractility signaling into a macroscale, tissue-level response. The microscale framework incorporates a biochemical signaling network along with characterization of fiber networks that govern the anisotropic mechanics of vascular tissue. By incorporating both biochemical and mechanical components, the model is more flexible and more broadly applicable to physiological and pathological conditions. The model can be applied to both cell and tissue scale systems, allowing for the analysis of in vitro, traction force microscopy and ex vivo, isometric contraction experiments in parallel. When applied to aortic explant rings and isolated smooth muscle cells, the model predicts that active contractility is not a function of stretch at intermediate strain. The model also successfully predicts cell-scale and tissue-scale contractility and matches experimentally observed behaviors, including the hypercontractile phenotype caused by chronic hyperglycemia. The connection of the microscale framework to the macroscale through the multiscale model presents a framework that can translate the wealth of information already collected at the cell scale to tissue scale phenotypes, potentially easing the development of smooth muscle cell-targeting therapeutics.

Vascular smooth muscle cell mechanotransduction through serum and glucocorticoid inducible kinase-1 promotes interleukin-6 production and macrophage accumulation in murine hypertension

Objective

The objective of this investigation was to demonstrate that in vivo induction of hypertension (HTN) and in vitro cyclic stretch of aortic vascular smooth muscle cells (VSMCs) can cause serum and glucocorticoid-inducible kinase (SGK-1)-dependent production of cytokines to promote macrophage accumulation that may promote vascular pathology.

Methods

HTN was induced in C57Bl/6 mice with angiotensin II infusion (1.46 mg/kg/day × 21 days) with or without systemic infusion of EMD638683 (2.5 mg/kg/day × 21 days), a selective SGK-1 inhibitor. Systolic blood pressure was recorded. Abdominal aortas were harvested to quantify SGK-1 activity (pSGK-1/SGK-1) by immunoblot. Flow cytometry quantified the abundance of CD11b+/F480+ cells (macrophages). Plasma interleukin (IL)-6 and monocyte chemoattractant protein-1 (MCP-1) was assessed by enzyme-linked immunosorbent assay. Aortic VSMCs from wild-type mice were subjected to 12% biaxial cyclic stretch (Stretch) for 3 or 12 hours with or without EMD638683 (10 μM) and with or without SGK-1 small interfering RNA with subsequent quantitative polymerase chain reaction for IL-6 and MCP-1 expression. IL-6 and MCP-1 in culture media were analyzed by enzyme-linked immunosorbent assay. Aortic VSMCs from SGK-1flox+/+ mice were transfected with Cre-Adenovirus to knockdown SGK-1 (SGK-1KD VSMCs) and underwent parallel tension experimentation. Computational modeling was used to simulate VSMC signaling. Statistical analysis included analysis of variance with significance at a P value of <.05.

Results

SGK-1 activity, abundance of CD11b+/F4-80+ cells, and plasma IL-6 were increased in the abdominal aorta of mice with HTN and significantly reduced by treatment with EMD638683. This outcome mirrored the increased abundance of IL-6 in media from Stretch C57Bl/6 VSMCs and attenuation of the effect with EMD638683 or SGK-1 small interfering RNA. C57Bl/6 VSMCs also responded to Stretch with increased MCP-1 expression and secretion into the culture media. Further supporting the integral role of mechanical signaling through SGK-1, target gene expression and cytokine secretion was unchanged in SGK-1KD VSMCs with Stretch, and computer modeling confirmed SGK-1 as an intersecting node of signaling owing to mechanical strain and angiotensin II.

Conclusions

Mechanical activation of SGK-1 in aortic VSMCs can promote inflammatory signaling and increased macrophage abundance, therefore this kinase warrants further exploration as a pharmacotherapeutic target to abrogate hypertensive vascular pathology.

Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein–Protein Interaction Model

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.

Experiment-based Computational Model Predicts that IL-6 Trans-Signaling Plays a Dominant Role in IL-6 mediated signaling in Endothelial Cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as peripheral arterial disease (PAD) and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and extracellular regulated kinase (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 trans-signaling induces stronger downstream signaling and plays a dominant role in the overall effects from IL-6. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the pSTAT3, pAkt, and pERK responses, which represent potential targets for inflammatory cytokine mediated signaling and angiogenesis-based therapies. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify targets that influence inflammatory cytokine mediated signaling in endothelial cells and to study the effects of angiogenesis- and inflammation-based therapies.

Organogermanium THGP Induces Differentiation into M1 Macrophages and Suppresses the Proliferation of Melanoma Cells via Phagocytosis

M1 macrophages are an important cell type related to tumor immunology and are known to phagocytose cancer cells. In previous studies, the organogermanium compound poly-trans-[(2-carboxyethyl)germasesquioxane] (Ge-132) and its hydrolysate, 3-(trihydroxygermyl) propanoic acid (THGP), have been reported to exert antitumor effects by activating NK cells and macrophages through the induction of IFN-γ activity in vivo. However, the detailed molecular mechanism has not been clarified. In this study, we found that macrophages differentiate into the M1 phenotype via NF-κB activation under long-term culture in the presence of THGP in vitro and in vivo. Furthermore, long-term culture with THGP increases the ability of RAW 264.7 cells to suppress B16 4A5 melanoma cell proliferation. These mechanisms indicate that THGP promotes the M1 polarization of macrophages and suppresses the expression of signal-regulatory protein alpha (SIRP-α) in macrophages and CD47 in cancers. Based on these results, THGP may be considered a new regulatory reagent that suppresses tumor immunity.

The cardiac wound healing response to myocardial infarction

Myocardial infarction (MI) is defined as evidence of myocardial necrosis consistent with prolonged ischemia. In response to MI, the myocardium undergoes a series of wound healing events that initiate inflammation and shift to anti-inflammation before transitioning to tissue repair that culminates in scar formation to replace the region of the necrotic myocardium. The overall response to MI is determined by two major steps, the first of which is the secretion of proteases by infiltrating leukocytes to breakdown extracellular matrix (ECM) components, a necessary step to remove necrotic cardiomyocytes. The second step is the generation of new ECM that comprises the scar; and this step is governed by the cardiac fibroblasts as the major source of new ECM synthesis. The leukocyte component resides in the middle of the two-step process, contributing to both sides as the leukocytes transition from pro-inflammatory to anti-inflammatory and reparative cell phenotypes. The balance between the two steps determines the final quantity and quality of scar formed, which in turn contributes to chronic outcomes following MI, including the progression to heart failure. This review will summarize our current knowledge regarding the cardiac wound healing response to MI, primarily focused on experimental models of MI in mice. This article is categorized under: Cardiovascular Diseases > Molecular and Cellular Physiology Immune System Diseases > Molecular and Cellular Physiology.

Intracellular signaling control of mechanical homeostasis in the aorta

Mature arteries exhibit a preferred biomechanical state in health evidenced by a narrow range of intramural and wall shear stresses. When stresses are perturbed by changes in blood pressure or flow, homeostatic mechanisms tend to restore target values via altered contractility and/or cell and matrix turnover. In contrast, vascular disease associates with compromised homeostasis, hence we must understand mechanisms underlying mechanical homeostasis and its robustness. Here, we use a multiscale computational model wherein mechanosensitive intracellular signaling pathways drive arterial growth and remodeling. First, we identify an ensemble of cell-level parameterizations where tissue-level responses are well-regulated and adaptive to hemodynamic perturbations. The responsible mechanism is persistent multiscale negative feedback whereby mechanosensitive signaling drives mass turnover until homeostatic target stresses are reached. This demonstrates how robustness emerges despite inevitable cell and individual heterogeneity. Second, we investigate tissue-level effects of signaling node knockdowns (ATIR, ROCK, TGF[Formula: see text]RII, PDGFR, ERK1/2) and find general agreement with experimental reports of fault tolerance. Robustness against structural changes manifests via low engagement of the node under baseline stresses or compensatory multiscale feedback via upregulation of additional pathways. Third, we show how knockdowns affect collagen and smooth muscle turnover at baseline and with perturbed stresses. In several cases, basal production is not remarkably affected, but sensitivities to stress deviations, which influence feedback strength, are reduced. Such reductions can impair adaptive responses, consistent with previously reported aortic vulnerability despite grossly normal appearances. Reduced stress sensitivities thus form a candidate mechanism for how robustness is lost, enabling transitions from health towards disease.

Artificial neural networks enable genome-scale simulations of intracellular signaling

Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.

Acute exposure to phthalates during recovery from a myocardial infarction induces greater inflammasome activation in male C57bl/6N mice

Plasticizers escape from medical devices used in cardiac surgery into patient blood and tissues. Increased di-ethylhexyl phthalate (DEHP) exposure is correlated with chronic inflammation in vivo and increased cytokine release in exposed monocytes in vitro. To determine if acute phthalate exposure enhanced inflammation in a model of cardiac damage, we measured immune cell infiltration, inflammasome expression and cardiac function in male C57bl/6 N mice exposed to phthalates during recovery from a surgically-induced myocardial infarction (MI). Phthalate exposed mice had greater neutrophil and pro-inflammatory macrophage infiltration, greater cardiac dilation and reduced cardiac function when compared with control mice. The greater expression of NLRP3 and NLRP6, but not AIM2 or P2xR7, in the infarcts of phthalate exposed versus control mice suggests a selectivity in pattern recognition receptor activation. Treatment of human THP-1 macrophages with phthalates revealed increased NLRP3 and NLRP6 expression and induction of a pro-inflammatory macrophage population. Pre-treatment with the PPARγ antagonist GW9662 reduced these increases. An increase in expression of IL-1R, MyD88 and IRAK4 in infarcts of phthalate exposed mice and THP-1 cells argues for greater priming downstream of IL-1R signaling and increased susceptibility for inflammasome activation. Importantly, these effects were moderated in vivo when phthalate exposure was reduced by 90% and when the NLRP3 antagonist MCC950 was co-administered. Our study suggests that reductions in phthalate exposure, which might be realized using plasticizers with a reduced ability to leach out from plastic, or short-term treatment with an anti-inflammasome may improve healing post-surgery.

Fibroblast mechanotransduction network predicts targets for mechano-adaptive infarct therapies

Regional control of fibrosis after myocardial infarction is critical for maintaining structural integrity in the infarct while preventing collagen accumulation in non-infarcted areas. Cardiac fibroblasts modulate matrix turnover in response to biochemical and biomechanical cues, but the complex interactions between signaling pathways confound efforts to develop therapies for regional scar formation. We employed a logic-based ordinary differential equation model of fibroblast mechano-chemo signal transduction to predict matrix protein expression in response to canonical biochemical stimuli and mechanical tension. Functional analysis of mechano-chemo interactions showed extensive pathway crosstalk with tension amplifying, dampening, or reversing responses to biochemical stimuli. Comprehensive drug target screens identified 13 mechano-adaptive therapies that promote matrix accumulation in regions where it is needed and reduce matrix levels in regions where it is not needed. Our predictions suggest that mechano-chemo interactions likely mediate cell behavior across many tissues and demonstrate the utility of multi-pathway signaling networks in discovering therapies for context-specific disease states.

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

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.

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).

Sphingosine-1-phosphate interactions in the spleen and heart reflect extent of cardiac repair in mice and failing human hearts

Sphingosine-1-phosphate (S1P) is a bioactive mediator in inflammation. Dysregulated S1P is demonstrated as a cause of heart failure (HF). However, the time-dependent and integrative role of S1P interaction with receptors in HF is unclear after myocardial infarction (MI). In this study, the sphingolipid mediators were quantified in ischemic human hearts. We also measured the time kinetics of these mediators post-MI in murine spleen and heart as an integrative approach to understand the interaction of S1P and respective S1P receptors in the transition of acute (AHF) to chronic HF (CHF). Risk-free 8-12 wk male C57BL/6 mice were subjected to MI surgery, and MI was confirmed by echocardiography and histology. Mass spectrometry was used to quantify sphingolipids in plasma, infarcted heart, spleen of mice, and ischemic and healthy human heart. The physiological cardiac repair was observed in mice with a notable increase of S1P quantity (pmol/g) in the heart and spleen significantly reduced in patients with ischemic HF. The circulating murine S1P levels were increased during AHF and CHF despite lowered substrate in CHF. The S1PR1 receptor expression was observed to coincide with the respective S1P quantity in mice and human hearts. Furthermore, selective S1P1 agonist limited inflammatory markers CCL2 and TNF-α and accelerated reparative markers ARG-1 and YM-1 in macrophages in the presence of Kdo2-Lipid A (KLA; potent inflammatory stimulant). This report demonstrated the importance of S1P/S1PR1 signaling in physiological inflammation during cardiac repair in mice. Alteration in these axes may serve as the signs of pathological remodeling in patients with ischemia.NEW & NOTEWORTHY Previous studies indicate that sphingosine-1-phosphate (S1P) has some role in cardiovascular disease. This study adds quantitative and integrative systems-based approaches that are necessary for discovery and bedside translation. Here, we quantitated sphinganine, sphingosine, sphingosine-1-phosphate (S1P) in mice and human cardiac pathobiology. Interorgan S1P quantity and respective systems-based receptor activation suggest cardiac repair after myocardial infarction. Thus, S1P serves as a therapeutic target for cardiac protection in clinical translation.

The Immunometabolic Roles of Various Fatty Acids in Macrophages and Lymphocytes

Macrophages and lymphocytes demonstrate metabolic plasticity, which is dependent partly on their state of activation and partly on the availability of various energy yielding and biosynthetic substrates (fatty acids, glucose, and amino acids). These substrates are essential to fuel-based metabolic reprogramming that supports optimal immune function, including the inflammatory response. In this review, we will focus on metabolism in macrophages and lymphocytes and discuss the role of fatty acids in governing the phenotype, activation, and functional status of these important cells. We summarize the current understanding of the pathways of fatty acid metabolism and related mechanisms of action and also explore possible new perspectives in this exciting area of research.

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.