Multiscale modeling has emerged as a powerful approach to interpret and capitalize on the biological complexity underlying blood vessel growth. We present a multiscale model of angiogenesis that heralds the start of a large scale initiative to integrate related biological models. The goal of the integrative project is to better understand underlying biological mechanisms from the molecular level up through the organ systems level, and test new therapeutic strategies. Model methodology includes ordinary and partial differential equations, stochastic models, complex logical rules, and agent-based architectures. Current modules represent blood flow, oxygen transport, growth factor distribution and signaling, cell sensing, cell movement and cell proliferation. Challenges of integration lie in connecting modules that are diversely designed, seamlessly coordinating feedback, and representing spatial and time scales from ligand-receptor interactions and intracellular signaling, to cell-level movement and cell-matrix interactions, to vessel branching and capillary network formation, to tissue level characteristics, to organ system response. We briefly introduce the individual modules, discuss our approach to integration, present initial results from the coordination of modules, and propose solutions to some critical issues facing angiogenesis multiscale modeling and integration.
Multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype. Here we describe the application of a multiscale modeling scheme to the HER3/ErbB3 receptor tyrosine kinase (RTK) signaling network, which regulates critical cellular processes including proliferation, migration and differentiation. The HER3 kinase is a topic of current interest and investigation, as it has been implicated in mechanisms of resistance to tyrosine kinase inhibition (TKI) of EGFR and HER2 in the treatment of many human malignancies. Moreover, the commonly regarded status of HER3 as a catalytically inactive ‘pseudokinase’ has recently been challenged by our previous study, which demonstrated robust residual kinase activity for HER3. Through our multiscale model, we investigate the most significant molecular interactions that contribute to potential mechanisms of HER3 activity and the physiological relevance of this activity to mechanisms of drug resistance in an ErbB-driven tumor cell in silico. The results of our molecular-scale simulations support the characterization of HER3 as a weakly active kinase that, in contrast to its fully-active ErbB family members, depends upon a unique hydrophobic interface to coordinate the alignment of specific catalytic residues required for its activity. Translating our molecular simulation results of the uniquely active behavior of the HER3 kinase into a physiologically relevant environment, our HER3 signaling model demonstrates that even a weak level of HER3 activity may be sufficient to induce AKT signaling and TKI resistance in the context of an ErbB signaling-dependent tumor cell, and therefore therapeutic targeting of HER3 may represent a superior treatment strategy for specific ErbB-driven cancers.
Viruses are infectious agents that can cause epidemics and pandemics. The understanding of virus formation, evolution, stability, and interaction with host cells is of great importance to the scientific community and public health. Typically, a virus complex in association with its aquatic environment poses a fabulous challenge to theoretical description and prediction. In this work, we propose a differential geometry-based multiscale paradigm to model complex biomolecule systems. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum domain of the fluid mechanical description of the aquatic environment from the microscopic discrete domain of the atomistic description of the biomolecule. A multiscale action functional is constructed as a unified framework to derive the governing equations for the dynamics of different scales. We show that the classical Navier-Stokes equation for the fluid dynamics and Newton's equation for the molecular dynamics can be derived from the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows.
In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-β1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-β1 literature–derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units (keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-β1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged (by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-β1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-β1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing.
Large chemical and biological systems such as fuel cells, ion channels, molecular motors, and viruses are of great importance to the scientific community and public health. Typically, these complex systems in conjunction with their aquatic environment pose a fabulous challenge to theoretical description, simulation, and prediction. In this work, we propose a differential geometry based multiscale paradigm to model complex macromolecular systems, and to put macroscopic and microscopic descriptions on an equal footing. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum mechanical description of the aquatic environment with the microscopic discrete atom-istic description of the macromolecule. Multiscale free energy functionals, or multiscale action functionals are constructed as a unified framework to derive the governing equations for the dynamics of different scales and different descriptions. Two types of aqueous macromolecular complexes, ones that are near equilibrium and others that are far from equilibrium, are considered in our formulations. We show that generalized Navier–Stokes equations for the fluid dynamics, generalized Poisson equations or generalized Poisson–Boltzmann equations for electrostatic interactions, and Newton's equation for the molecular dynamics can be derived by the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows. Comparison is given to classical descriptions of the fluid and electrostatic interactions without geometric flow based micro-macro interfaces. The detailed balance of forces is emphasized in the present work. We further extend the proposed multiscale paradigm to micro-macro analysis of electrohydrodynamics, electrophoresis, fuel cells, and ion channels. We derive generalized Poisson–Nernst–Planck equations that are coupled to generalized Navier–Stokes equations for fluid dynamics, Newton's equation for molecular dynamics, and potential and surface driving geometric flows for the micro-macro interface. For excessively large aqueous macromolecular complexes in chemistry and biology, we further develop differential geometry based multiscale fluid-electro-elastic models to replace the expensive molecular dynamics description with an alternative elasticity formulation.
Variational principle; Multiscale; Geometric flows; Solvation analysis; Electrostatic analysis; Implicit solvent models; Molecular dynamics; Elasticity; Navier–Stokes equation; Poisson–Boltzmann equation; Nernst–Planck equation
Glioblastoma multiforme (GBM) has a dismal prognosis despite aggressive therapy. Initial diagnosis and measurement of response to treatment is usually determined by measurement of gadolinium-enhanced tumor volume with magnetic resonance imaging (MRI). Unfortunately, many GBM treatment modalities can cause changes in tumor gadolinium enhancement patterns that mimic tumor progression. The lack of a definitive imaging modality to distinguish posttreatment radiographic imaging changes (PTRIC), including pseudoprogression and radiation necrosis, from true tumor progression presents a major unmet clinical need in the management of GBM patients.
The authors discuss current modalities available for differentiating PTRIC and tumor progression, describe development of an animal model of PTRIC, and consider potential molecular and cellular pathways involved in the development of PTRIC.
An animal model using glioma cells transfected with a luciferase reporter has been developed, and after conventional GBM therapy, this animal model can be evaluated with posttreatment bioluminescence imaging and various MR tumor imaging modalities.
Posttreatment radiographic changes that mimic tumor progression can influence clinicians to make treatment decisions that are inappropriate for the patient's actual clinical condition. Several imaging modalities have been used to try to distinguish PTRIC and true progression, including conventional MRI, perfusion MRI, MR spectroscopy, and positron emission tomography (PET); however, none of these modalities has consistently and reliably distinguished PTRIC from tumor growth. An animal model using glioma cells transfected with a luciferase reporter may enable mechanistic studies to determine causes and potential treatments for PTRIC.
Brain; chemotherapy; imaging; models; pseudoprogression; radiation injury; rat
Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem.
We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis.
This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.
Proton transport is one of the most important and interesting phenomena in living cells. The present work proposes a multiscale/multiphysics model for the understanding of the molecular mechanism of proton transport in transmembrane proteins. We describe proton dynamics quantum mechanically via a density functional approach while implicitly model other solvent ions as a dielectric continuum to reduce the number of degrees of freedom. The densities of all other ions in the solvent are assumed to obey the Boltzmann distribution. The impact of protein molecular structure and its charge polarization on the proton transport is considered explicitly at the atomic level. We formulate a total free energy functional to put proton kinetic and potential energies as well as electrostatic energy of all ions on an equal footing. The variational principle is employed to derive nonlinear governing equations for the proton transport system. Generalized Poisson-Boltzmann equation and Kohn-Sham equation are obtained from the variational framework. Theoretical formulations for the proton density and proton conductance are constructed based on fundamental principles. The molecular surface of the channel protein is utilized to split the discrete protein domain and the continuum solvent domain, and facilitate the multiscale discrete/continuum/quantum descriptions. A number of mathematical algorithms, including the Dirichlet to Neumann mapping, matched interface and boundary method, Gummel iteration, and Krylov space techniques are utilized to implement the proposed model in a computationally efficient manner. The Gramicidin A (GA) channel is used to demonstrate the performance of the proposed proton transport model and validate the efficiency of proposed mathematical algorithms. The electrostatic characteristics of the GA channel is analyzed with a wide range of model parameters. The proton conductances are studied over a number of applied voltages and reference concentrations. A comparison with experimental data verifies the present model predictions and validates the proposed model.
Proton transport; quantum dynamics in continuum; multiscale model; Poisson-Boltzmann equation; generalized Kohn-Sham equation; variational principle
Glioblastoma multiforme (GBM) is a highly invasive and chemoradioresistant brain malignancy. Temozolomide (TMZ), a DNA-alkylating agent, is effective against GBM and has become the standard first-line drug. However, the mechanism by which TMZ regulates the progression of GBM remains elusive. Here, we demonstrate that TMZ targets TAp63, a p53 family member, inducing its expression to suppress the progression of human GBM. High levels of TAp63 expression in GBM tissues after TMZ treatment was an indicator of favourable prognosis. In human GBM cells, TMZ-induced TAp63 directly repressed MYC transcription. Activation of this TAp63-MYC pathway by TMZ inhibited human GBM progression both in vitro and in vivo. Furthermore, downregulation of MYC mRNA levels in recurrent GBMs after TMZ treatment correlated with better patient survival. Therefore, our results suggest that the TAp63-mediated transcriptional repression of MYC is a novel pathway regulating TMZ efficacy in GBM.
The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silico modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment.
We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether.
Our in silico results indicate that in NSCLC, in the presence of a strong extrinsic chemotactic stimulus (and depending on the cell's location) downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.
Glioblastoma multiforme (GBM), an aggressive brain tumor of astrocytic/neural stem cell origin, represents one of the most incurable cancers. GBM tumors are highly heterogeneous. However, most tumors contain a subpopulation of cells that display neural stem cell characteristics in vitro and that can generate a new brain tumor upon transplantation in mice. Hence, previously identified molecular pathways regulating neural stem cell biology were found to represent the cornerstone of GBM stem cell self-renewal mechanism. GBM tumors are also notorious for their resistance to radiation therapy. Notably, GBM “cancer stem cells” were also found to be responsible for this radioresistance. Herein, we will analyze the data supporting or not the cancer stem cell model in GBM, overview the current knowledge regarding GBM stem cell self-renewal and radioresistance molecular mechanisms, and discuss the potential therapeutic application of these findings.
polycomb, BMI1; cancer stem cell; glioma; glioblastoma multiforme; astrocyte; astrocytoma; radioresistance; CD133; prominin
The complexity in intracellular signaling mechanisms relevant for the conquest of many diseases resides at different levels of organization with scales ranging from the subatomic realm relevant to catalytic functions of enzymes to the mesoscopic realm relevant to the cooperative association of molecular assemblies and membrane processes. Consequently, the challenge of representing and quantifying functional or dysfunctional modules within the networks remains due to the current limitations in our understanding of mesoscopic biology, i.e., how the components assemble into functional molecular ensembles. A multiscale approach is necessary to treat a hierarchy of interactions ranging from molecular (nm, ns) to signaling (μm, ms) length and time scales, which necessitates the development and application of specialized modeling tools. Complementary to multiscale experimentation (encompassing structural biology, mechanistic enzymology, cell biology, and single molecule studies) multiscale modeling offers a powerful and quantitative alternative for the study of functional intracellular signaling modules. Here, we describe the application of a multiscale approach to signaling mediated by the ErbB1 receptor which constitutes a network hub for the cell’s proliferative, migratory, and survival programs. Through our multiscale model, we mechanistically describe how point-mutations in the ErbB1 receptor can profoundly alter signaling characteristics leading to the onset of oncogenic transformations. Specifically, we describe how the point mutations induce cascading fragility mechanisms at the molecular scale as well as at the scale of the signaling network to preferentially activate the survival factor Akt. We provide a quantitative explanation for how the hallmark of preferential Akt activation in cell-lines harboring the constitutively active mutant ErbB1 receptors causes these cell-lines to be addicted to ErbB1-mediated generation of survival signals. Consequently, inhibition of ErbB1 activity leads to a remarkable therapeutic response in the addicted cell lines.
Tumorigenic potential of glioblastoma multiforme (GBM) cells is, in part, attributable to their undifferentiated (neural stem cell-like) phenotype. Astrocytic differentiation of GBM cells is associated with transcriptional induction of Glial Fibrillary Acidic Protein (GFAP) and repression of Nestin, whereas the reciprocal transcription program operates in undifferentiated GBM cells. The molecular mechanisms underlying the regulation of these transcription programs remain elusive. Here, we show that the transcriptional co-activator p300 was expressed in GBM tumors and cell lines and acted as an activator of the GFAP gene and a repressor of the Nestin gene. On the other hand, Myc (formerly known as c-Myc overrode these p300 functions by repressing the GFAP gene and inducing the Nestin gene in GBM cells. Moreover, RNAi-mediated inhibition of p300 expression significantly enhanced the invasion potential of GBM cells in vitro. Taken together, these data suggest that dedifferentiated/undifferentiated GBM cells are more invasive than differentiated GBM cells. Because invasion is a major cause of GBM morbidity, differentiation therapy may improve the clinical outcome.
glioblastoma multiforme (GBM); GBM stem cell; differentiation; invasion; p300; Myc
Human glioblastoma (GBM) cells are notorious for their resistance to apoptosis-inducing therapeutics. We have identified lanatoside C as a sensitizer of GBM cells to tumor necrosis factor–related apoptosis-inducing ligand (TRAIL)–induced cell death partly by upregulation of the death receptor 5. We show that lanatoside C sensitizes GBM cells to TRAIL-induced apoptosis in a GBM xenograft model in vivo. Lanatoside C on its own serves as a therapeutic agent against GBM by activating a caspase-independent cell death pathway. Cells treated with lanatoside C showed necrotic cell morphology with absence of caspase activation, low mitochondrial membrane potential, and early intracellular ATP depletion. In conclusion, lanatoside C sensitizes GBM cells to TRAIL-induced cell death and mitigates apoptosis resistance of glioblastoma cells by inducing an alternative cell death pathway. To our knowledge, this is one of the first examples of use of caspase-independent cell death inducers to trigger tumor regression in vivo. Activation of such mechanism may be a useful strategy to counter resistance of cancer cells to apoptosis.
cardiac glycoside; glioblastoma; lanatoside C; non-apoptotic cell death; TRAIL
Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli’s metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors.
Anaplastic astrocytoma (AA) and its more aggressive counterpart, glioblastoma multiforme (GBM), are the most common intrinsic brain tumors in adults and are almost universally fatal. A deeper understanding of the molecular relationship of these tumor types is necessary to derive insights into the diagnosis, prognosis, and treatment of gliomas. Although genomewide profiling of expression levels with microarrays can be used to identify differentially expressed genes between these tumor types, comparative studies so far have resulted in gene lists that show little overlap.
To achieve a more accurate and stable list of the differentially expressed genes and pathways between primary GBM and AA, we performed a meta-analysis using publicly available genome-scale mRNA data sets. There were four data sets with sufficiently large sample sizes of both GBMs and AAs, all of which coincidentally used human U133 platforms from Affymetrix, allowing for easier and more precise integration of data. After scoring genes and pathways within each data set, we combined the statistics across studies using the nonparametric rank sum method to identify the features that differentiate GBMs and AAs. We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested. We also used the rank sum approach to select >20 significant Biocarta pathways after correction for multiple testing out of >175 pathways examined. The most significant pathway was the hypoxia-inducible factor (HIF) pathway. Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA.
We have performed a meta-analysis of genome-scale mRNA expression data for 289 human malignant gliomas and have identified a list of >900 probe sets and >20 pathways that are significantly different between GBM and AA. These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology. More generally, this approach suggests that combined analysis of existing data sets can reveal new insights and that the large amount of publicly available cancer data sets should be further utilized in a similar manner.
Adaptation to hypoxia and consequent pro-inflammatory gene expression of prostate and breast carcinomas have been implicated in the progression toward cancer malignant phenotype. Only partial data are available for the human tumor glioblastoma multiforme (GBM). The aim of our study was to analyze the hypoxic and pro-inflammatory microenvironment in GBMs and to demonstrate that in a stem/progenitor cell line derived from human glioblastoma (GBM-SCs), hypoxia activates a coordinated inflammatory response, evidencing an invasive and migratory phenotype.
From each of 10 human solid glioblastomas, clinically and histopathologically characterized, we obtained three surgical samples taken from the center and the periphery of the tumor, and from adjacent host normal tissue. Molecular and morphological analyses were carried out using quantitative real-time PCR and western blot (WB). GBM stem and differentiated cells were incubated under hypoxic conditions and analyzed for pro-inflammatory gene expression and for invasive/migratory behavior.
A panel of selected representative pro-inflammatory genes (RAGE and P2X7R, COX2, NOS2 and, PTX3) were analyzed, comparing tumor, peritumor and host normal tissues. Tumors containing leukocyte infiltrates (as assessed using CD45 immunohistochemistry) were excluded. Selected genes were overexpressed in the central regions of the tumors (i.e. in the more hypoxic areas), less expressed in peripheral regions, and poorly expressed or absent in adjacent normal host tissues. Western blot analysis confirmed that the corresponding pro-inflammatory proteins were also differently expressed. Hypoxic stem cell lines showed a clear time-dependent activation of the entire panel of pro-inflammatory genes as compared to differentiated tumor cells. Biological assays showed that invasive and migratory behavior was strengthened by hypoxia only in GBM stem cells.
In human solid glioblastoma we have observed a coordinated overexpression of a panel of pro-inflammatory genes as compared to host normal tissue. We have also evidenced a similar pattern of overexpressed genes in GBM-SCs after hypoxic treatment, showing also a gain of invasive and migratory function that was lost when these stem cells differentiated. We suggest that, as has been previously described for prostatic and mammary carcinoma, in human glioblastoma acquisition of a proinflammatory phenotype may be relevant for malignant progression.
Increasing the sensitivity of glioblastoma cells to radiation is a promising approach to improve survival in patients with glioblastoma multiforme (GBM). This study aims to determine if serine/threonine phosphatase (protein phosphatase 6 (PP6)) is a molecular target for GBM radiosensitization treatment. The GBM orthotopic xenograft mice model was used in this study. Our data demonstrated that the protein level of PP6 catalytic subunit (PP6c) was upregulated in the GBM tissue from about 50% patients compared with the surrounding tissue or control tissue. Both the in vitro survival fraction of GBM cells and the patient survival time were highly correlated or inversely correlated with PP6c expression (R2=0.755 and −0.707, respectively). We also found that siRNA knockdown of PP6c reduced DNA-dependent protein kinase (DNA-PK) activity in three different GBM cell lines, increasing their sensitivity to radiation. In the orthotopic mice model, the overexpression of PP6c in GBM U87 cells attenuated the effect of radiation treatment, and reduced the survival time of mice compared with the control mice, while the PP6c knocking-down improved the effect of radiation treatment, and increased the survival time of mice. These findings demonstrate that PP6 regulates the sensitivity of GBM cells to radiation, and suggest small molecules disrupting or inhibiting PP6 association with DNA-PK is a potential radiosensitizer for GBM.
PP6; GBM; radiation resistance; DNA-PK
Recurrent glioblastomas (rGBM) invariably relapse after initial response to anti-VEGF therapy. There are two prevailing hypotheses on how these tumors escape antiangiogenic therapy: switch to VEGF-independent angiogenic pathways and vessel co-option. However, direct evidence in rGBM patients is lacking. Thus, we compared molecular, cellular and vascular parameters in autopsy tissues from five rGBM patients who had been treated with the pan-VEGF receptor tyrosine kinase inhibitor cediranib versus seven patients who received no therapy or chemoradiation but no antiangiogenic agents. After cediranib treatment, endothelial proliferation and glomeruloid vessels were decreased, and vessel diameters and perimeters were reduced to levels comparable to the unaffected contralateral brain hemisphere. In addition, tumor endothelial cells expressed molecular markers specific to the blood-brain barrier, indicative of a lack of revascularization despite the discontinuation of therapy. Surprisingly, in cediranib-treated GBM cellular density in the central area of the tumor was lower than in control cases and gradually decreased towards the infiltrating edge, indicative of a change in growth pattern of rGBMs after cediranib treatment, unlike that after chemo-radiation. Finally, cediranib treated GBMs showed high levels of PDGF-C and c-Met expression and infiltration by myeloid cells, which may potentially contribute to resistance to anti-VEGF therapy. In summary, we show that rGBMs switch their growth pattern after anti-VEGF therapy – characterized by lower tumor cellularity in the central area, decreased pseudopalisading necrosis and blood vessels with normal molecular expression and morphology without a second wave of angiogenesis.
The morphogenetic movements, and the embryonic phenotypes they ultimately produce, are the consequence of a series of events that involve signaling pathways, cytoskeletal components, and cell- and tissue-level mechanical interactions. In order to better understand how these events work together in the context of amphibian neurulation, an existing multiscale computational model was augmented. Geometric data for this finite element-based mechanical model were obtained from 3D surface reconstructions of live axolotl embryos and serial sections of fixed specimens. Tissue mechanical properties were modeled using cell-based constitutive equations that include internal force generation and cell rearrangement, and equation parameters were adjusted manually to reflect biochemical changes including alterations in Shroom or the planar-cell-polarity pathway. The model indicates that neural tube defects can arise when convergent extension of the neural plate is reduced by as little as 20%, when it is eliminated on one side of the embryo, when neural ridge elevation is disrupted, when tension in the non-neural ectoderm is increased, or when the ectoderm thickness is increased. Where comparable conditions could be induced in Xenopus embryos, good agreement was found, an important step in model validation. The model reveals the neurulating embryo to be a finely tuned biomechanical system.
Dimerization of transmembrane (TM) α helices of membrane receptors plays a key role in signaling. We show that molecular dynamics simulations yield models of integrin TM helix heterodimers, which agree well with available NMR structures. We use a multiscale simulation approach, combining coarse-grained and subsequent atomistic simulation, to model the dimerization of wild-type (WT) and mutated sequences of the αIIb and β3 integrin TM helices. The WT helices formed a stable, right-handed dimer with the same helix-helix interface as in the published NMR structure (PDB: 2K9J). In contrast, the presence of disruptive mutations perturbed the interface between the helices, altering the conformational stability of the dimer. The αIIb/β3 interface was more flexible than that of, e.g., glycophorin A. This is suggestive of a role for alternative packing modes of the TM helices in transbilayer signaling.
► Multiscale simulation have been used to model transmembrane helix heterodimers ► This method is applied to the integrin αIIb/β3 heterodimer ► The model exhibits right-handed packing of the helices, in agreement with NMR structures ► Flexibility of the αIIb/β3 interface suggests of a role for alternative packing modes in transbilayer signaling.
The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalysing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus or treated with IFNγ, we show that, under these assumptions, coordinate down-regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of down-regulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses.
► We model the cholesterol biosynthesis pathway and its regulation. ► The innate immune response leads to a suppression of flux through the pathway. ► Statin inhibitors show a different mode of suppression to the immune response. ► Statin inhibitor suppression is less robust and less specific than immune suppression.
Cholesterol; Systems biology; Regulation; Anti-viral; Statin
Several small molecules that inhibit the PI3 kinase (PI3K)-Akt signaling pathway are in clinical development. Although many of these molecules have been effective in preclinical models, it remains unclear whether this strategy alone will be sufficient to interrupt the molecular events initiated and maintained by signaling along the pathways because of the activation of other pathways that compensate for the inhibition of the targeted kinase. In this study, we performed a synthetic lethality screen to identify genes or pathways whose inactivation, in combination with the PI3K inhibitors PX-866 and NVPBEZ-235, might result in a lethal phenotype in glioblastoma multiforme (GBM) cells. We screened GBM cells (U87, U251, and T98G) with a large-scale, short hairpin RNA library (GeneNet), which contains 43 800 small interfering RNA sequences targeting 8500 well-characterized human genes. To decrease off-target effects, we selected overlapping genes among the 3 cell lines that synergized with PX-866 to induce cell death. To facilitate the identification of potential targets, we used a GSE4290 dataset and The Cancer Genome Atlas GBM dataset, identifying 15 target genes overexpressed in GBM tissues. We further analyzed the selected genes using Ingenuity Pathway Analysis software and showed that the 15 genes were closely related to cancer-promoting pathways, and a highly interconnected network of aberrations along the MYC, P38MAPK, and ERK signaling pathways were identified. Our findings suggest that inhibition of these pathways might increase tumor sensitivity to PX-866 and therefore represent a potential clinical therapeutic strategy.
glioma; shRNA library; synthetic lethality
Geometric modeling of biomolecules plays an essential role in the conceptualization of biolmolecular structure, function, dynamics and transport. Qualitatively, geometric modeling offers a basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. Quantitatively, geometric modeling bridges the gap between molecular information, such as that from X-ray, NMR and cryo-EM, and theoretical/mathematical models, such as molecular dynamics, the Poisson-Boltzmann equation and the Nernst-Planck equation. In this work, we present a family of variational multiscale geometric models for macromolecular systems. Our models are able to combine multiresolution geometric modeling with multiscale electrostatic modeling in a unified variational framework. We discuss a suite of techniques for molecular surface generation, molecular surface meshing, molecular volumetric meshing, and the estimation of Hadwiger’s functionals. Emphasis is given to the multiresolution representations of biomolecules and the associated multiscale electrostatic analyses as well as multiresolution curvature characterizations. The resulting fine resolution representations of a biomolecular system enable the detailed analysis of solvent-solute interaction, and ion channel dynamics, while our coarse resolution representations highlight the compatibility of protein-ligand bindings and possibility of protein-protein interactions.
Variational multiscale modeling; Multiresolution surface; Energy functional; Meshing; Curvature; Electrostatics
There is a need to develop multiscale models of vascular adaptations to understand tissue level manifestations of cellular level mechanisms. Continuum based biomechanical models are well suited for relating blood pressures and flows to stress-mediated changes in geometry and properties, but less so for describing underlying mechanobiological processes. Discrete stochastic agent based models are well suited for representing biological processes at a cellular level, but not for describing tissue level mechanical changes. We present here a conceptually new approach to facilitate the coupling of continuum and agent based models. Because of ubiquitous limitations in both the tissue- and cell-level data from which one derives constitutive relations for continuum models and rule-sets for agent based models, we suggest that model verification should enforce congruency across scales. That is, multiscale model parameters initially determined from data sets representing different scales should be refined, when possible, to ensure that common outputs are consistent. Potential advantages of this approach are illustrated by comparing simulated aortic responses to a sustained increase in blood pressure predicted by continuum and agent based models both before and after instituting a genetic algorithm to refine 16 objectively bounded model parameters. We show that congruency-based parameter refinement not only yielded increased consistency across scales, it also yielded predictions that are closer to in vivo observations.
Agent Based Model (ABM); Constrained Mixture Model (CMM); Growth and Remodeling; Model Verification