Integrative Biology

Data on human skin fibroblast transcriptional responses to external cues were used to reconstruct dynamic gene regulatory networks. The goal of the reconstruction was to determine dynamic network interactions (quantitative predictive relationships of mutual regulatory influences of and on transcription factor expression) from time course data on 56 transcript expression levels obtained following different external cues. The inherently under-determined nature of this problem was addressed in part by excluding putative regulatory motifs that did not appear to be functional in multiple independent experiments from different independent external perturbations. Data were obtained from a previously published experiment in which the 56 transcripts were assayed by bioluminescence in live cells cultured on substrates of varying levels of stiffness and exposed to different levels of arginylglycylaspartic acid (RGD) peptide. The inferred dynamical networks were validated via comparison of predictions to a priori known interactions from gene databases. We discovered that exposures to different substrate stiffnesses and to RGD stimulate responses that are mediated through GATA4, SMAD3/4, ETS-1, and STAT5 and other genes, which can initiate hypertrophic, fibrotic, and inflammatory responses. The developed dynamical system identification method for discovering new mechanotransduction pathways is applicable to the identification of gene regulatory networks in numerous emerging applications where time-series data on multiple state variables and from multiple external perturbations are available.

We identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity. Multi-modal experimental data from TAMs exposed in vitro to a series of perturbations were collected to build a Boolean dynamical network of TAMs. This framework successfully identified the biological pathways underlying TAM pro-metastatic activity and predicted potent pharmacological interventions that inhibited the pro-metastatic activity of TAMs isolated from patient tumors. This study showcases the power of integrating computational predictions with experimental validation in identifying new therapeutic avenues that can be extended to other cancer types.

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.

Chemotaxis drives critical processes in cancer metastasis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze chemotaxis at single-cell resolution. Here we focus on chemotaxis driven by the CXCL12-CXCR4 pathway, a signaling network that promotes metastasis in more than 20 different human cancers. CXCL12-CXCR4 activates ERK and Akt, kinases known to promote chemotaxis, but how cells couple signaling to chemotaxis remain poorly defined. To address this challenge, we implemented single-cell analysis of MDA-MB-231 breast cancer cells migrating in a chemotaxis device towards chemokine CXCL12. We integrated live, single-cell imaging with advanced computational analysis methods to discover processes defining subsets of cells that move efficiently toward a CXCL12 gradient. We identified dynamic oscillations in ERK and Akt signaling and associated morphological transitions as key determinants of successful chemotaxis. Cells with effective chemotaxis toward CXCL12 exhibit faster and more persistent movement than non-migrating cells, but both cell populations show similar random motion. Migrating cells exhibit higher amplitude fluctuations in ERK and Akt signaling and greater frequencies of generating lateral cell membrane protrusions. Interestingly, computational analysis reveals less correlated network coupling of signaling and morphological changes in migrating cells. These data reveal processing events that enable cells to convert a signaling input to chemotaxis and highlight how cells in a uniform environment produce heterogeneous responses.

The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) receptors, two clinically relevant targets for immunotherapy of cancer, are negative regulators of in immune cell activation and migration. However, optimizing therapeutic outcomes still requires fundamental research to reach a comprehensive insight into the coherent function of immune regulators. Here, we investigated the statistical dynamics of T cells migration as a measure of the functional response to these pathways in an experimental setup of immune checkpoint blockade. For this purpose, we used a previously developed 3-dimensional organotypic culture of patient-derived tumor spheroids. Experiment-based dynamical modeling remarked distinct characteristics of the receptors regulation followed through with the modification of their proportions in the immune modulation. We demonstrated that time-delayed kinetics of PD-1 activation just overrides its relatively more efficient cell-level function which potentially makes an operative contribution to the functional dominance of CTLA-4 in the tumor microenvironment. Simulation results showed good agreement with data for tumor cells reduction and active immune cells count observed in each experiment. These analyses propose a new mechanistic view on relative immunogenicity of PD-1 and CTLA-4 inhibitors manifested in literature and point the possible inherent obstacles in checkpoint inhibition-based immunotherapy of cancer to address in the future. SignificanceEx vivo monitoring of temporal response to PD-1 and CTLA-4 in the closure of T cell movement dynamics and elucidating their feasible commitment to the kinetic constraints at cell-level resolution. Delayed dynamics of migratory response to CTLA-4 inhibition revealed a mechanistic view on potential T cell reinvigoration following immune checkpoint blockade.

Tumor necrosis factor alpha (TNF), a pleiotropic cytokine, helps maintain a balance between proliferation and apoptosis in normal cells. This balance is often sacrificed in a diseased cell, such as that of a cancer, by preferring survival phenotype over apoptosis. Restoring this balance requires a detailed understanding of the causal intracellular mechanisms that govern TNF stimulated apoptotic response. In this study, we use a systems biology approach to unravel the interplay between the intracellular signaling markers that orchestrate apoptosis levels. Our approach deciphered the synergism between the early intracellular markers phosphorylated JNK (pJNK) and phosphorylated AKT (pAKT) that modulate the activation of Caspase3, an important apoptotic regulator. We demonstrate that this synergism depends critically on the survival pathway signaling mediated by NF{kappa}B which plays a dominant role in controlling the extent of the overall apoptotic response. By systematic inhibition of the signaling markers, we establish that the dynamic cross-talk between the pJNK and pAKT transients directs the apoptosis phenotype via accumulated Caspase3 response. Interestingly, superposition of the semi-quantitative correlation between apoptosis and Caspase3 transient levels on the proposed TNF network model permits quantification of the dynamic apoptotic response under different stimulation conditions. Thus, the predictive model can be leveraged towards arriving at useful insights that can identify potential targeted therapeutic strategies for altering apoptotic response.

Angiogenesis, the formation of new vessels from existing vessels, is mediated by vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF). Despite discoveries supporting the cross-family interactions between VEGF and PDGF families, sharing the binding partners between them makes it challenging to identify growth factors that predominantly affect angiogenesis. Systems biology offers promises to untangle this complexity. Thus, in this study, we developed a mass-action kinetics-based computational model for cross-family interactions between VEGFs (VEGF-A, VEGF-B, and PlGF) and PDGFs (PDGF-AA, PDGF-AB, and PDGF-BB) with their receptors (VEGFR1, VEGFR2, NRP1, PDGFR, and PDGFR{beta}). The model, parametrized with our literature mining and surface resonance plasmon assays, was validated by comparing the concentration of VEGFR1 complexes with a previously constructed angiogenesis model. The model predictions include five outcomes: 1) the percentage of free or bound ligands and 2) receptors, 3) the concentration of free ligands, 4) the percentage of ligands occupying each receptor, and 5) the concentration of ligands that is bound to each receptor. We found that at equimolar ligand concentrations (1 nM), PlGF and VEGF-A were the main binding partners of VEGFR1 and VEGFR2, respectively. Varying the density of receptors resulted in the following five outcomes: 1) Increasing VEGFR1 density depletes the free PlGF concentration, 2) increasing VEGFR2 density decreases PDGF:PDGFR complexes, 3) increased NRP1 density generates a biphasic concentration of the free PlGF, 4) increased PDGFR density increases PDGFs:PDGFR binding, and 5) increasing PDGFR{beta} density increases VEGF-A:PDGFR{beta}. Our model offers a reproducible, fundamental framework for exploring cross-family interactions that can be extended to the tissue level or intracellular molecular level. Also, our model may help develop therapeutic strategies in pathological angiogenesis by identifying the dominant complex in the cell signaling. Author summaryNew blood vessel formation from existing ones is essential for growth, healing, and reproduction. However, when this process is disrupted--either too much or too little--it can contribute to diseases such as cancer and peripheral arterial disease. Two key families of proteins, vascular endothelial growth factors (VEGFs) and platelet-derived growth factors (PDGFs), regulate this process. Traditionally, scientists believed that VEGFs only bind to VEGF receptors and PDGFs to PDGF receptors. However, recent findings show that these proteins can interact with each others receptors, making it more challenging to understand and control blood vessel formation. To clarify these complex interactions, we combined computer modeling with biological data to map out which proteins bind to which receptors and to what extent. Our findings show that when VEGFs and PDGFs are present in equal amounts, VEGFs are the primary binding partners for VEGF receptors. We also explored how changes in receptor levels affect these interactions in disease-like conditions. This work provides a foundational computational model for studying cross-family interactions, which can be expanded to investigate tissue-level effects and processes inside cells. Ultimately, our model may help develop better treatments for diseases linked to abnormal blood vessel growth by identifying key protein-receptor interactions.

Collective cell migration, a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal migrations. Here, we constitute a tumor core of two separate cell subsets showing distinct intra-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). Our simulations show that for a given two intra-subset cell-cell adhesions for two subsets of cells, low and high inter-subset cell adhesion favors migration of high HI and low HI intermingled populations, respectively. In addition, for the most explored values of cell-ECM adhesion strengths, populations with high HI values collectively migrate better than those with lower HI values. We then asked how spatial migration is regulated by progressively intermingled cellular subsets that were epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and migration. We also observe an exception to this relationship for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively migrate better than their relatively higher HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies migrative dynamics through collective cellular unjamming, when either cell-cell or -ECM adhesion type is varied but incorporates dispersion when both adhesion types are simultaneously altered.

Brain cells are influenced by continuous fluid shear stress driven by varying hydrostatic and osmotic pressure conditions, depending on the brains pathophysiological conditions. While all brain cells are sensitive to the subtle changes in various physicochemical factors in the microenvironment, microglia, the resident brain immune cells, exhibit the most dramatic morphodynamic transformation. However, little is known about the phenotypic alterations in microglia in response to the changes in fluid shear stress. In this study, we first established a flow-controlled microenvironment to investigate the effects of shear flow on microglial phenotypes, including morphology, motility, and activation states. Microglia exhibited two distinct morphologies with different migratory phenotypes in a static condition: bipolar cells that oscillate along their long axis and unipolar cells that migrate persistently. When exposed to flow, a significant fraction of bipolar cells showed unstable oscillation with an increased amplitude of oscillation and a decreased frequency, which consequently led to the phenotypic transformation of oscillating cells into migrating cells. Interestingly, the level of pro-inflammatory genes increased in response to shear stress, while there were no significant changes in the level of anti-inflammatory genes. Our findings suggest that an interstitial fluid-level stimulus can cause a dramatic phenotypic shift in microglia toward pro-inflammatory states, shedding light on pathological outbreaks of severe brain diseases. Given that the fluidic environment in the brain can be locally disrupted in pathological circumstances, the mechanical stimulus by a fluid flow should also be considered a crucial element in regulating the immune activities of the microglia in brain diseases. Statement of SignificanceCellular morphology and motility are important factors that encompass the alterations in protein and gene-level expressions within cells. In pathological conditions, microglia, the resident brain immune cells, are known to undergo morphodynamic transformations in response to various physicochemical stimuli. Besides the commonly known soluble biochemical factors in the microenvironment, the differential flow characteristics of ISF have been linked to several neurological diseases, such as Alzheimers, Parkinsons, and brain tumors. Microglial cells, which are extremely sensitive to subtle changes in extracellular stimuli, have been identified as key players in these pathological conditions. Despite its importance, however, it has been challenging to study the sole effect of a shear flow on microglia. We investigated the morphodynamic features of microglia in response to precisely controlled interstitial-level fluid flow conditions using a microfluidic system in which isolated microglia are monitored in real-time while the undesirable effects from other extracellular factors are minimized.

Macrophages in the tumor microenvironment (TME) can constitute up to 50% of tumor mass and play a critical role in cancer cell proliferation, invasion, and metastasis. While their contribution to extracellular matrix (ECM) degradation through matrix metalloproteinases (MMPs) has been explored, the role of other macrophage-derived factors in ECM remodeling and their impacts beyond degradation remain poorly understood. Here, we describe the development of a 3D collagen-based tumor spheroid model to investigate the impact of peripheral blood mononuclear cell (PBMC)-derived macrophages on cancer cell-ECM and cancer cell-macrophage interactions within the TME. We observed that cancer cells stimulated PBMC-derived macrophages into an M2-like phenotype and that tumor spheroid conditioned macrophages (TSCMs) shifted cancer cell populations toward phenotypes with greater invasion distances and reduced circularity, indicative of increased malignancy. Such observations can be explained by macrophage-mediated ECM remodeling. Specifically, we demonstrate that TSCMs secreted a variety of soluble factors that are known to contribute to ECM remodeling, including ECM degradation and fiber realignment. These processes collectively create a tumor-favoring environment by loosening the collagen matrix and aligning fibers that serve as invasion tracks for migrating tumor cells that facilitate cancer cell migration and invasion. This model provides a robust platform to study the interactions between cellular and non-cellular components in the TME and to identify the molecular mechanisms underlying cancer progression. These insights may aid in the development of novel therapeutic strategies targeting macrophage-mediated processes in cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/679587v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@17d1f9forg.highwire.dtl.DTLVardef@1a71500org.highwire.dtl.DTLVardef@53deeeorg.highwire.dtl.DTLVardef@510268_HPS_FORMAT_FIGEXP M_FIG C_FIG

Although several cancer cells are shed into the circulation, most die due to the stresses they encounter. Detachment from the underlying extracellular matrix is one major stress; cell death due to matrix detachment is known as anoikis. Few cancer cells overcome this stress, become anoikis-resistant, and survive to seed metastasis. Autophagy, a cell survival mechanism during stressful conditions that promotes cellular homeostasis by recycling cellular components, is activated upon matrix detachment. On the other hand, apoptosis is a cellular mechanism that is responsible for programmed cell death which is also activated upon matrix detachment. It is unclear how matrix-deprived cancer cells maintain a balance between autophagy and apoptosis to decide the cell fate: whether the cell dies due to anoikis or acquires anoikis-resistance and survives to seed metastasis. Though multiple pathways contribute to cell fate decisions, we have shown experimentally that autophagy and apoptosis are influenced by Akt-AMPK axis and AMPK-ERK axis in matrix-deprived cancer cells. Since Akt, AMPK and ERK are in turn linked to each other it is essential to understand how these proteins simultaneously affect the cell signaling and survival/death outcomes of matrix-deprived cancer cells. To study the cumulative effect of Akt-AMPK-ERK activities on survival/death decisions and to understand the fine balance between apoptosis and autophagy that facilitates the survival/death of matrix-deprived cancer cells, we formulated a deterministic ordinary differential equation (ODE)-based protein interaction model of anoikis resistance. Model stability analysis and 3D-nullcline analysis depicted that the system has a unique steady state in matrix-attached and matrix-deprived condition. Parameter sensitivity analysis depicted that the model is highly robust, and the model variables are sensitive to only a few model parameters. By simulating differential activity of pAkt, pAMPK and pERK, the model predicted a heterogeneity in pERK levels: high/low levels pERK along with high pAMPK enable survival as long as levels of pAkt are maintained low. Additionally, the model predicted a heterogeneity in pAMPK: high/low levels of pAMPK along with low pERK determines the shift from survival to death when levels of pAkt are high. Such high levels of pAkt are obtained at critically low levels of pERK. Molecular perturbation revealed a hierarchy among proteins while deciding the cell fate: pAkt dominates over pAMPK which further dominates over pERK and intermediate to high levels of pAkt were sufficient for apoptosis to surpass autophagy in matrix-detached cells. The model also predicted that Akt impacts apoptosis more than autophagy and classified the cell fate decision into survival and death zones in matrix-deprived condition. Overall, this work provided multiple insights on the molecular interplay among key kinases Akt, AMPK and ERK and their effects on apoptosis and autophagy. This model also depicted that autophagosome formation is rather robust as compared to apoptosis which is more sensitive to molecular perturbations. Hence, apoptosis emerged as a deciding factor that influences the decision of cell fate of a matrix-deprived cancer cell.

Chemotaxis plays a critical role in the metastatic progression of breast cancer. The chemokine CXCL12 is well recognized as an essential component of chemotactic migration in triple-negative breast cancer (TNBC) cells in vivo. The purpose of this study is to determine how the highly metastatic TNBC cell line, MDA-MB-231, migrates in response to well-defined CXCL12 gradients in vitro. Traditional 2D transwell migration assays were optimized to gauge the MDA-MB-231 cells responsiveness to various CXCL12 concentrations. The optimum chemoattractant concentrations were applied to a commercially available 3D chemotaxis assay as stable linearly diffused gradients. Cells were embedded in type 1 bovine collagen at two different collagen concentrations, and individual unlabeled cells were monitored for 24 hours using brightfield microscopy. Time-lapse videos were used to track cell movement and shape. Quantitative data analysis was performed using an automated tracking software to measure chemotactic parameters based on cell morphology. MDA-MB-231 cells were responsive to CXCL12 concentrations greater than 200 ng/mL in 2D and 3D systems. In 3D systems, significant directed migration was observed in denser collagen matrices. It was observed that in 3D matrices a range of cell morphologies was present. Therefore, chemotaxis was evaluated as a function of cell shape revealing some differences between sub cellular populations. Our findings show the cells shape influences the chemotactic sensing towards CXCL12 gradients.

In biology, oscillations are observed across a wide spectrum of processes and systems. Oscillatory systems are typically leveraged to transmit information within cells. However, they can also serve to transmit information between organisms underscoring their fundamental role in regulating transitions, maintaining stability, and responding to environmental stimuli. In this study, we explore mitochondrial fission and fusion dynamics through the framework of the Belousov-Zhabotinsky (BZ) reaction, a hallmark of non-equilibrium system that exhibits periodic changes in reactant concentrations through autocatalysis and feedback regulation. We observed that mitochondrial changes followed an oscillatory dynamic where the fission, fusion and intermediary factors undergo oscillations. Also, by modelling comparison with publicly available datasets of diseased condition before and after therapy, we observed similarities, where under diseased condition there is increased concentration of the fission and fusion factors but upon treatment the concentration of the intermediary factors increase. Also, patient survival data analysis showed that increase in fission and fusion factors correlated with increased deaths but when there is increase in the intermediary factor concentration, we see better patient survival. These results highlight the possibility of targeting mitochondrial dynamics as a potential strategy for therapeutic development for diseases such as cancer where mitochondrial dynamics is dysregulated.

Cross-family signaling between platelet-derived growth factors (PDGFs) and vascular endothelial growth factor receptors (VEGFRs) is a newly discovered yet unexplored mechanism of angiogenesis regulation. This study elucidates the role of PDGFs in endothelial cell (EC) signaling and functions, focusing on VEGFR1 and VEGFR2 activation. Using human dermal microvascular ECs (HDMECs) with double knockout of PDGFR/{beta} and human brain microvascular ECs (HBMECs), we show three key findings: (1) PDGF-AA and -BB induced VEGFR1 phosphorylation, peaking at 2-fold increases at low concentrations (0.5 ng/mL), while PDGF-AB stimulated a 2-fold rise in VEGFR2 phosphorylation. (2) Downstream effectors PLC{gamma}1, Akt, and FAK were activated by all three PDGFs at levels comparable to VEGF-A. (3) PDGF-BB significantly enhanced EC proliferation (up to 240%) and migration (up to 170%), with lower PDGF concentrations (0.5-5 ng/mL) eliciting stronger effects than higher concentrations (50-100 ng/mL). Overall, PDGF subtypes differentially induce VEGFR phosphorylation, downstream effector activation, and angiogenic hallmarks such as proliferation and migration, revealing novel mechanisms for regulating endothelial function.

Cancer immunotherapy offers lifesaving treatments for cancers, but the lack of reliable preclinical models that could enable the mechanistic studies of tumor-immune interactions hampers the identification of new therapeutic strategies. We hypothesized 3D confined microchannels, formed by interstitial space between bio-conjugated liquid-like solids (LLS), enable CAR T dynamic locomotion within an immunosuppressive TME to carry out anti-tumor function. Murine CD70-specific CAR T cells cocultured with the CD70-expressing glioblastoma and osteosarcoma demonstrated efficient trafficking, infiltration, and killing of cancer cells. The anti-tumor activity was clearly captured via longterm in situ imaging and supported by upregulation of cytokines and chemokines including IFNg, CXCL9, CXCL10, CCL2, CCL3, and CCL4. Interestingly, target cancer cells, upon an immune attack, initiated an "immune escape" response by frantically invading the surrounding microenvironment. This phenomenon however was not observed for the wild-type tumor samples which remained intact and produced no relevant cytokine response. Single cells collection and transcriptomic profiling of CAR T cells at regions of interest revealed feasibility of identifying differential gene expression amongst the immune subpopulations. Complimentary 3D in vitro platforms are necessary to uncover cancer immune biology mechanisms, as emphasized by the significant roles of the TME and its heterogeneity.

BackgroundIntercellular communication is a critical innovation during multicellular organismal evolution. Cells release / receive cytokines and utilize them as intercellular signal entities. How a well-orchestrated communication emerges from individual cell behavior remains largely unknown. Here we abstracted the biological phenomenon and developed multi-agent-simulation to investigate the intracellular communication. MethodsTwo dimensional MAS platform was developed using Artisoc 4.2.1 standard software. We focused on intercellular communication via cytokines and extracellular matrices. Three agents, "cells", "cytokines" and "extracellular matrices" were defined and the interaction rules among the agents were designed. Two different mathematical models of cytokine-gradient determination were tested: spatial derivative and temporal derivative models. As a case study, neutrophil swarming was modeled and the cell swarming was defined as an evaluation criterion. Moreover, a surgically injured mouse model and a fluorescent time-lapse imaging were used to observe neutrophil swarming. ResultsWe performed multiple simulations with six different virtual conditions, changing multiple parameters simultaneously and randomly. After 400 simulations for each condition, we counted the number of successful trials (i.e. neutrophil swarming within 10000 steps). Spatial derivative model showed more successes compared to temporal derivative model. Among eight parameters randomly assigned, cells exploration speed by random walk most remarkably influenced on success rate of neutrophil swarming. In in vivo model, bone marrow derived neutrophils gather towards the clumps with various migration speed. The mode of approaching resembles to spatial derivative model, rather than temporal derivative model. ConclusionsMAS could be a useful approach to investigate the emergence of intercellular communication during multicellular evolution. Neutrophil could adopt the spatial derivative model as a sensing mechanism of cytokine gradient.

The endometrium and menstrual disorders, such as endometriosis and adenomyosis, are difficult to study, partly because menstruation depends on interactions between multiple cell types through complex molecular mechanisms. To help understand this system, researchers need humanized experimental and computational models that can interrogate how endometrial cell populations impact each other in both physiological and pathological conditions. Here, we use ordinary differential equations (ODEs) to model changes in the rates of endometrial cell proliferation and death in response to hormones, cytokines, and the specific cell types present. To calibrate this computational model, we used previous-published experimental datasets from a 3D co-culture platform supporting primary human endometrial epithelial organoids and endometrial stromal cells. Our ODE-based model can simulate the size of endometrial epithelial organoids and the density of stromal cells over time under multiple hormone/cytokine conditions in mono- and co-cultures. We further created a second, partial differential equation (PDE)-based model that simulates the diffusion of molecules added to these 3D cultures and their uptake by proliferating endometrial cells using the predicted cell densities from the ODE model as inputs to the PDE simulations. We show that the exposure to hormones and cytokines used in the experiments is reasonably homogenous throughout the 3D culture and identify conditions where this would not be true. Altogether we use these models to quantify the influence of stromal cells on epithelial cell proliferation and vice versa, to identify differences across cells from different donors, and to provide a quantitative assessment of experimental designs.

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of Epithelial-Mesenchymal Transition (EMT) with the Androgen Receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer. Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse out-comes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting prostate cancer aggressiveness.

Interstitial fluid flow plays a critical role in maintaining function and homeostasis in neural tissue, and dysregulation of this flow due to injury or disease results in mechanical stress that is associated with several neuropathologies, including traumatic brain injury, ischemic stroke, and glioma. Glial cells such as astrocytes and microglia are known to respond to mechanical forces like fluid shear stress but the impact of this stress on their functionality and any subsequent impact on neurons remains poorly defined. To investigate how pathologically high fluid shear stress modulates astrocyte and microglia function and to determine whether glial responses to fluid shear influence neuronal survival and morphology, we applied low pathological levels of fluid shear stress (0.1 dynes/cm2) to cultured human astrocytes and microglia and assessed functional changes including metabolic activity, metabolite release, lipid droplet accumulation, and phagocytic activity. Conditioned media from these glia were then applied to differentiated SH-SY5Y neurons to evaluate effects on cell survival and neurite outgrowth. We then focused on identifying the soluble factor mediating the observed neurotoxicity. We found that fluid shear stress promotes distinct functional responses in astrocytes and microglia, including increased metabolic activity in astrocytes, increased lipid droplet accumulation in microglia, and heightened release of extracellular ATP in both cell types. Exposure to shear-conditioned glial media significantly reduced neuronal survival and neurite length. This neurotoxic effect was abolished by activated charcoal filtration but not by boiling and was prevented through P2x7 receptor inhibition in neurons, suggesting extracellular ATP as a causative agent. These findings indicate that high fluid shear stress promotes glial-mediated neurotoxicity via purinergic signaling. This study helps to characterize glial-neuronal mechanobiological interaction in the context of neuropathology and provides support for targeting purinergic signaling pathways as a therapeutic approach for neuropathologies associated with altered interstitial fluid flow. Statement of significanceGlial cells, until recently merely considered to support neurons, are now known to play critical roles in neural tissue development and function. Astrocytes and microglia play diverse roles in the central nervous system, adopting phenotypes that both promote and resolve pathology. Within brain tissue, disruptions to interstitial fluid flow are associated with brain injury, ischemic stroke, and glioma. This study identifies fluid shear stress as a modulator of glial cell function with downstream neurotoxic consequences. Namely, we found that increased release of extracellular ATP by glial cells under high shear promotes neurotoxicity via P2x7 receptor signaling. These findings help to characterize mechanobiological glial-neuronal communication and lend support for the therapeutic efficacy of P2x7 receptor inhibition in the treatment of neuropathology.