Adhesive dynamics simulation of G-protein-mediated chemokine-activated neutrophil adhesion

Localization of Rolling and Firm-Adhesive Interactions Between Circulating Tumor Cells and the Microvasculature Wall

INTRODUCTION

The adhesion of tumor cells to vessel wall is a critical stage in cancer metastasis. Firm adhesion of cancer cells is usually followed by their extravasation through the endothelium. Despite previous studies identifying the influential parameters in the adhesive behavior of the cancer cell to a planer substrate, less is known about the interactions between the cancer cell and microvasculature wall and whether these interactions exhibit organ specificity. The objective of our study is to characterize sizes of microvasculature where a deformable circulating cell (DCC) would firmly adhere or roll over the wall, as well as to identify parameters that facilitate such firm adherence and underlying mechanisms driving adhesive interactions.

METHODS

A three-dimensional model of DCCs is applied to simulate the fluid-structure interaction between the DCC and surrounding fluid. A dynamic adhesion model, where an adhesion molecule is modeled as a spring, is employed to represent the stochastic receptor-ligand interactions using kinetic rate expressions.

RESULTS

Our results reveal that both the cell deformability and low shear rate of flow promote the firm adhesion of DCC in small vessels ( < 10 μ m ). Our findings suggest that ligand-receptor bonds of PSGL-1-P-selectin may lead to firm adherence of DCC in smaller vessels and rolling-adhesion of DCC in larger ones where cell velocity drops to facilitate the activation of integrin-ICAM-1 bonds.

CONCLUSIONS

Our study provides a framework to predict accurately where different DCC-types are likely to adhere firmly in microvasculature and to establish the criteria predisposing cancer cells to such firm adhesion.

Identifying Key Pathways and Components in Chemokine-Triggered T Lymphocyte Arrest Dynamics Using a Multi-Parametric Global Sensitivity Analysis

Introduction

The arrest of rolling T lymphocytes at specific locations is crucial to proper immune response function. We previously developed a model of chemokine-driven integrin activation, termed integrative signaling adhesive dynamics (ISAD). In addition, we have shown that loss of diacylglycerol kinase (DGK) leads to a gain of function regarding adhesion under shear flow. We undertook this study to understand the sensitivity of adhesion to perturbations in other signaling molecules.

Methods

We adapted multi-parametric sensitivity analysis (MPSA) for use in our ISAD model to identify important parameters, including initial protein concentrations and kinetic rate constants, for T lymphocyte arrest. We also compared MPSA results to those obtained from a single parametric sensitivity analysis.

Results

In addition to the previously shown importance of DGK in lymphocyte arrest, PIP₂ cleavage and Rap1 activation are crucial in determining T cell arrest dynamics, which agree with previous experimental findings. The l-selectin density on the T lymphocyte surface also plays a large role in determining the distance rolled before arrest. Both the MPSA and single-parametric method returned similar results regarding the most sensitive kinetic rate constants.

Conclusion

We show here that the regulation of the amount of second messengers are, in general, more critical for determining T lymphocyte arrest over the initial signaling proteins, highlighting the importance of amplification of signaling in cell adhesion responses. Overall, this work provides a mechanistic insight of the contribution of key pathways and components, thus may help to identify potential therapeutic targets for drug development against immune disorders.

Adhesive dynamics

Adhesive dynamics (AD) is a method for simulating the dynamic response of biological systems in response to force. Biological bonds are mechanical entities that exert force under strain, and applying forces to biological bonds modulates their rate of dissociation. Since small numbers of events usually control biological interactions, we developed a simple method for sampling probability distributions for the formation or failure of individual bonds. This method allows a simple coupling between force and strain and kinetics, while capturing the stochastic response of biological systems. Biological bonds are dynamically reconfigured in response to applied mechanical stresses, and a detailed spatio-temporal map of molecules and the forces they exert emerges from AD. The shape or motion of materials bearing the molecules is easily calculated from a mechanical energy balance provided the rheology of the material is known. AD was originally used to simulate the dynamics of adhesion of leukocytes under flow, but new advances have allowed the method to be extended to many other applications, including but not limited to the binding of viruses to surface, the clustering of adhesion molecules driven by stiff substrates, and the effect of cell-cell interaction on cell capture and rolling dynamics. The technique has also been applied to applications outside of biology. A particular exciting recent development is the combination of signaling with AD (so-called integrated signaling adhesive dynamics, or ISAD), which allows facile integration of signaling networks with mechanical models of cell adhesion and motility. Potential opportunities in applying AD are summarized.

Multiscale computational models of complex biological systems

Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental methodologies, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems, using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to leverage these multiscale models to gain insight into biological systems using quantitative biomedical engineering methods to analyze data in nonintuitive ways. These topics are discussed with a focus on the future of the field, current challenges encountered, and opportunities yet to be realized.

Computational and experimental models of cancer cell response to fluid shear stress

It has become evident that mechanical forces play a key role in cancer metastasis, a complex series of steps that is responsible for the majority of cancer-related deaths. One such force is fluid shear stress, exerted on circulating tumor cells by blood flow in the vascular microenvironment, and also on tumor cells exposed to slow interstitial flows in the tumor microenvironment. Computational and experimental models have the potential to elucidate metastatic behavior of cells exposed to such forces. Here, we review the fluid-generated forces that tumor cells are exposed to in the vascular and tumor microenvironments, and discuss recent computational and experimental models that have revealed mechanotransduction phenomena that may play a role in the metastatic process.

Icam-1 upregulation in ethanol-induced Fatty murine livers promotes injury and sinusoidal leukocyte adherence after transplantation

Background. Transplantation of ethanol-induced steatotic livers causes increased graft injury. We hypothesized that upregulation of hepatic ICAM-1 after ethanol produces increased leukocyte adherence, resulting in increased generation of reactive oxygen species (ROS) and injury after liver transplantation (LT). Methods. C57BL/6 wildtype (WT) and ICAM-1 knockout (KO) mice were gavaged with ethanol (6 g/kg) or water. LT was then performed into WT recipients. Necrosis and apoptosis, 4-hydroxynonenal (4-HNE) immunostaining, and sinusoidal leukocyte movement by intravital microscopy were assessed. Results. Ethanol gavage of WT mice increased hepatic triglycerides 10-fold compared to water treatment (P < 0.05). ICAM-1 also increased, but ALT was normal. At 8 h after LT of WT grafts, ALT increased 2-fold more with ethanol than water treatment (P < 0.05). Compared to ethanol-treated WT grafts, ALT from ethanol-treated KO grafts was 78% less (P < 0.05). Apoptosis also decreased by 75% (P < 0.05), and 4-HNE staining after LT was also decreased in ethanol-treated KO grafts compared to WT. Intravital microscopy demonstrated a 2-fold decrease in leukocyte adhesion in KO grafts compared to WT grafts. Conclusions. Increased ICAM-1 expression in ethanol-treated fatty livers predisposes to leukocyte adherence after LT, which leads to a disturbed microcirculation, oxidative stress and graft injury.

Delivery of drugs to the brain via the blood brain barrier using colloidal carriers

Delivering drugs to the brain is challenging given the selective permeability of the blood brain barrier (BBB). Targeted colloidal carriers containing drug payloads offer some promise for enhanced and perhaps selective delivery to brain. This review examines the recent literature and identifies issues to be addressed if these systems are to be rationally designed. These include opsonization of nanoparticles and off-target clearance; the cerebral microvasculature, flow of nanoparticles in capillaries and binding to the capillary wall; and transcytosis. Capillary architecture, blood flow and BBB permeability are affected by disease and age and there are species differences. These complexities caution against making extravagant claims for a particular nanosystem but they also highlight the rich opportunities and need for critical research in this field.

An integrated stochastic model of "inside-out" integrin activation and selective T-lymphocyte recruitment

The pattern of T-lymphocyte homing is hypothesized to be controlled by combinations of chemokine receptors and complementary chemokines. Here, we use numerical simulation to explore the relationship among chemokine potency and concentration, signal transduction, and adhesion. We have developed a form of adhesive dynamics-a mechanically accurate stochastic simulation of adhesion-that incorporates stochastic signal transduction using the next subvolume method. We show that using measurable parameter estimates derived from a variety of sources, including signaling measurements that allow us to test parameter values, we can readily simulate approximate time scales for T-lymphocyte arrest. We find that adhesion correlates with total chemokine receptor occupancy, not the frequency of occupation, when multiple chemokine receptors feed through a single G-protein. A general strategy for selective T-lymphocyte recruitment appears to require low affinity chemokine receptors. For a single chemokine receptor, increases in multiple cross-reactive chemokines can lead to an overwhelming increase in adhesion. Overall, the methods presented here provide a predictive framework for understanding chemokine control of T-lymphocyte recruitment.

Stochastic models of cell protrusion arising from spatiotemporal signaling and adhesion dynamics

During cell migration, local protrusion events are regulated by biochemical and physical processes that are in turn coordinated with the dynamic properties of cell-substratum adhesion structures. In this chapter, we present a modeling approach for integrating the apparent stochasticity and spatial dependence of signal transduction pathways that promote protrusion in tandem with adhesion dynamics. We describe our modeling framework, as well as its abstraction, parameterization, and validation against experimental data. Analytical techniques for identifying and evaluating the effects of model bistability on simulation simulation results are shown, and implications of this analysis for understanding cell protrusion behavior are offered.

Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion

The LFA-1 integrin plays a pivotal role in sustained leukocyte adhesion to the endothelial surface, which is a precondition for leukocyte recruitment into inflammation sites. Strong correlative evidence implicates LFA-1 clustering as being essential for sustained adhesion, and it may also facilitate rebinding events with its ligand ICAM-1. We cannot challenge those hypotheses directly because it is infeasible to measure either process during leukocyte adhesion following rolling. The alternative approach undertaken was to challenge the hypothesized mechanisms by experimenting on validated, working counterparts: simulations in which diffusible, LFA1 objects on the surfaces of quasi-autonomous leukocytes interact with simulated, diffusible, ICAM1 objects on endothelial surfaces during simulated adhesion following rolling. We used object-oriented, agent-based methods to build and execute multi-level, multi-attribute analogues of leukocytes and endothelial surfaces. Validation was achieved across different experimental conditions, in vitro, ex vivo, and in vivo, at both the individual cell and population levels. Because those mechanisms exhibit all of the characteristics of biological mechanisms, they can stand as a concrete, working theory about detailed events occurring at the leukocyte-surface interface during leukocyte rolling and adhesion experiments. We challenged mechanistic hypotheses by conducting experiments in which the consequences of multiple mechanistic events were tracked. We quantified rebinding events between individual components under different conditions, and the role of LFA1 clustering in sustaining leukocyte-surface adhesion and in improving adhesion efficiency. Early during simulations ICAM1 rebinding (to LFA1) but not LFA1 rebinding (to ICAM1) was enhanced by clustering. Later, clustering caused both types of rebinding events to increase. We discovered that clustering was not necessary to achieve adhesion as long as LFA1 and ICAM1 object densities were above a critical level. Importantly, at low densities LFA1 clustering enabled improved efficiency: adhesion exhibited measurable, cell level positive cooperativity.