Endothelial cells metabolically regulate breast cancer invasion toward a microvessel.

Breast cancer metastasis is initiated by invasion of tumor cells into the collagen type I-rich stroma to reach adjacent blood vessels. Prior work has identified that metabolic plasticity is a key requirement of tumor cell invasion into collagen. However, it remains largely unclear how blood vessels affect this relationship. Here, we developed a microfluidic platform to analyze how tumor cells invade collagen in the presence and absence of a microvascular channel. We demonstrate that endothelial cells secrete pro-migratory factors that direct tumor cell invasion toward the microvessel. Analysis of tumor cell metabolism using metabolic imaging, metabolomics, and computational flux balance analysis revealed that these changes are accompanied by increased rates of glycolysis and oxygen consumption caused by broad alterations of glucose metabolism. Indeed, restricting glucose availability decreased endothelial cell-induced tumor cell invasion. Our results suggest that endothelial cells promote tumor invasion into the stroma due, in part, to reprogramming tumor cell metabolism.

Integrated Constraint-Based Modeling of E. coli Cell-Free Protein Synthesis.

Cell-free protein expression has become a widely used research tool in systems and synthetic biology and a promising technology for protein biomanufacturing. Cell-free protein synthesis relies on in-vitro transcription and translation processes to produce a protein of interest. However, transcription and translation depend upon the operation of complex metabolic pathways for precursor and energy regeneration. Toward understanding the role of metabolism in a cell-free system, we developed a dynamic constraint-based simulation of protein production in the myTXTL E. coli cell-free system with and without electron transport chain inhibitors. Time-resolved absolute metabolite measurements for â"³ = 63 metabolites, along with absolute concentration measurements of the mRNA and protein abundance and measurements of enzyme activity, were integrated with kinetic and enzyme abundance information to simulate the time evolution of metabolic flux and protein production with and without inhibitors. The metabolic flux distribution estimated by the model, along with the experimental metabolite and enzyme activity data, suggested that the myTXTL cell-free system has an active central carbon metabolism with glutamate powering the TCA cycle. Further, the electron transport chain inhibitor studies suggested the presence of oxidative phosphorylation activity in the myTXTL cell-free system; the oxidative phosphorylation inhibitors provided biochemical evidence that myTXTL relied, at least partially, on oxidative phosphorylation to generate the energy required to sustain transcription and translation for a 16-hour batch reaction.

Convergent Approaches to Delineate the Metabolic Regulation of Tumor Invasion by Hyaluronic Acid Biosynthesis.

Metastasis is the leading cause of breast cancer-related deaths and is often driven by invasion and cancer-stem like cells (CSCs). Both the CSC phenotype and invasion are associated with increased hyaluronic acid (HA) production. How these independent observations are connected, and which role metabolism plays in this process, remains unclear due to the lack of convergent approaches integrating engineered model systems, computational tools, and cancer biology. Using microfluidic invasion models, metabolomics, computational flux balance analysis, and bioinformatic analysis of patient data, the functional links between the stem-like, invasive, and metabolic phenotype of breast cancer cells as a function of HA biosynthesis are investigated. These results suggest that CSCs are more invasive than non-CSCs and that broad metabolic changes caused by overproduction of HA play a role in this process. Accordingly, overexpression of hyaluronic acid synthases (HAS) 2 or 3 induces a metabolic phenotype that promotes cancer cell stemness and invasion in vitro and upregulates a transcriptomic signature predictive of increased invasion and worse patient survival. This study suggests that HA overproduction leads to metabolic adaptations to satisfy the energy demands for 3D invasion of breast CSCs highlighting the importance of engineered model systems and multidisciplinary approaches in cancer research.

Constructing Cell-Free Expression Systems for Low-Cost Access.

Cell-free systems for gene expression have gained attention as platforms for the facile study of genetic circuits and as highly effective tools for teaching. Despite recent progress, the technology remains inaccessible for many in low- and middle-income countries due to the expensive reagents required for its manufacturing, as well as specialized equipment required for distribution and storage. To address these challenges, we deconstructed processes required for cell-free mixture preparation and developed a set of alternative low-cost strategies for easy production and sharing of extracts. First, we explored the stability of cell-free reactions dried through a low-cost device based on silica beads, as an alternative to commercial automated freeze dryers. Second, we report the positive effect of lactose as an additive for increasing protein synthesis in maltodextrin-based cell-free reactions using either circular or linear DNA templates. The modifications were used to produce active amounts of two high-value reagents: the isothermal polymerase Bst and the restriction enzyme BsaI. Third, we demonstrated the endogenous regeneration of nucleoside triphosphates and synthesis of pyruvate in cell-free systems (CFSs) based on phosphoenol pyruvate (PEP) and maltodextrin (MDX). We exploited this novel finding to demonstrate the use of a cell-free mixture completely free of any exogenous nucleotide triphosphates (NTPs) to generate high yields of sfGFP expression. Together, these modifications can produce desiccated extracts that are 203-424-fold cheaper than commercial versions. These improvements will facilitate wider use of CFS for research and education purposes.

Effective Biophysical Modeling of Cell Free Transcription and Translation Processes.

Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

Engineering orthogonal human O-linked glycoprotein biosynthesis in bacteria.

A major objective of synthetic glycobiology is to re-engineer existing cellular glycosylation pathways from the top down or construct non-natural ones from the bottom up for new and useful purposes. Here, we have developed a set of orthogonal pathways for eukaryotic O-linked protein glycosylation in Escherichia coli that installed the cancer-associated mucin-type glycans Tn, T, sialyl-Tn and sialyl-T onto serine residues in acceptor motifs derived from different human O-glycoproteins. These same glycoengineered bacteria were used to supply crude cell extracts enriched with glycosylation machinery that permitted cell-free construction of O-glycoproteins in a one-pot reaction. In addition, O-glycosylation-competent bacteria were able to generate an antigenically authentic Tn-MUC1 glycoform that exhibited reactivity with antibody 5E5, which specifically recognizes cancer-associated glycoforms of MUC1. We anticipate that the orthogonal glycoprotein biosynthesis pathways developed here will provide facile access to structurally diverse O-glycoforms for a range of important scientific and therapeutic applications.

Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli.

In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Improving designer glycan production in Escherichia coli through model-guided metabolic engineering.

Asparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogen Campylobacter jejuni and its subsequent transfer into laboratory strains of Escherichia coli has spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model of E. coli metabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated with C. jejuni glycan assembly into a genome-scale model of E. coli metabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

Analysis of the role of thrombomodulin in all-trans retinoic acid treatment of coagulation disorders in cancer patients.

BACKGROUND: Clinical studies have shown that all-trans retinoic acid (RA), which is often used in treatment of cancer patients, improves hemostatic parameters and bleeding complications such as disseminated intravascular coagulation (DIC). However, the mechanisms underlying this improvement have yet to be elucidated. In vitro studies have reported that RA upregulates thrombomodulin (TM) expression on the endothelial cell surface. The objective of this study was to investigate how and to what extent the TM concentration changes after RA treatment in cancer patients, and how this variation influences the blood coagulation cascade. RESULTS: In this study, we introduced an ordinary differential equation (ODE) model of gene expression for the RA-induced upregulation of TM concentration. Coupling the gene expression model with a two-compartment pharmacokinetic model of RA, we obtained the time-dependent changes in TM and thrombomodulin-mRNA (TMR) concentrations following oral administration of RA. Our results indicated that the TM concentration reached its peak level almost 14 h after taking a single oral dose (110 [Formula: see text]) of RA. Continuous treatment with RA resulted in oscillatory expression of TM on the endothelial cell surface. We then coupled the gene expression model with a mechanistic model of the coagulation cascade, and showed that the elevated levels of TM over the course of RA therapy with a single daily oral dose (110 [Formula: see text]) of RA, reduced the peak thrombin levels and endogenous thrombin potential (ETP) up to 50 and 49%, respectively. We showed that progressive reductions in plasma levels of RA, observed in continuous RA therapy with a once-daily oral dose (110 [Formula: see text]) of RA, did not affect TM-mediated reduction of thrombin generation significantly. This finding prompts the hypothesis that continuous RA treatment has more consistent therapeutic effects on coagulation disorders than on cancer. CONCLUSIONS: Our results indicate that the oscillatory upregulation of TM expression on the endothelial cells over the course of RA therapy could potentially contribute to the treatment of coagulation abnormalities in cancer patients. Further studies on the impacts of RA therapy on the procoagulant activity of cancer cells are needed to better elucidate the mechanisms by which RA therapy improves hemostatic abnormalities in cancer.

Retinoic acid and 6-formylindolo(3,2-b)carbazole (FICZ) combination therapy reveals putative targets for enhancing response in non-APL AML.

In non-acute promyelotic leukemia (APL)- non myelocytic leukemia (AML), identification of a signaling signature would predict potentially actionable targets to enhance differentiation effects of all-trans-retinoic acid (RA) and make combination differentiation therapy realizable. Components of such a signaling machine/signalsome found to drive RA-induced differentiation discerned in a FAB M2 cell line/model (HL-60) were further characterized and then compared against AML patient expression profiles. FICZ, known to enhance RA-induced differentiation, was used to experimentally augment signaling for analysis. FRET revealed novel signalsome protein associations: CD38 with pS376SLP76 and caveolin-1 with CD38 and AhR. The signaling molecules driving differentiation in HL-60 cluster in non-APL AML de novo samples, too. Pearson correlation coefficients for this molecular ensemble are nearer 1 in the FAB M2 subtype than in non-APL AML. SLP76 correlation to RXRα and p47phox were conserved in FAB M2 model and patient subtype but not in general non-APL AML. The signalsome ergo identifies potential actionable targets in AML.

Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors.

Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis.

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis, and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature; thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of carbon number and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multiprotein synthetic circuits, RNA circuits, or the cell-free production of small molecule products.

Src family kinase inhibitor bosutinib enhances retinoic acid-induced differentiation of HL-60 leukemia cells.

The acute promyelocytic leukemia (APL) has been treated with all-trans retinoic acid (RA) for decades. While RA has largely been ineffective in non-APL AML subtypes, co-treatments combining RA and other agents are currently in clinical trials. Using the RA-responsive non-APL AML cell line HL-60, we tested the efficacy of the Src family kinase (SFK) inhibitor bosutinib on RA-induced differentiation. HL-60 has been recently shown to bear fidelity to a subtype of AML that respond to RA. We found that co-treatment with RA and bosutinib enhanced differentiation evidenced by increased CD11b expression, G1/G0 cell cycle arrest, and respiratory burst. Expression of the SFK members Fgr and Lyn was enhanced, while SFK activation was inhibited. Phosphorylation of several sites of c-Raf was increased and expression of AhR and p85 PI3K was enhanced. Expression of c-Cbl and mTOR was decreased. Our study suggests that SFK inhibition enhances RA-induced differentiation and may have therapeutic value in non-APL AML.

Potential for subsets of wt-NPM1 primary AML blasts to respond to retinoic acid treatment.

Acute myeloid leukemia (AML) has high mortality rates, perhaps reflecting a lack of understanding of the molecular diversity in various subtypes and a lack of known actionable targets. There are currently 12 open clinical trials for AML using combination therapeutic modalities including all-trans retinoic acid (RA). Mutant nucleophosmin-1, proposed as a possible marker for RA response, is the criterion for recruiting patients in three active RA phase 3 clinical trials. We tested the ability of RA alone or in combination with either bosutinib (B) or 6-formylindolo(3,2-b) carbazole (F) to induce conversion of 12 de novo AML samples toward a more differentiated phenotype. We assessed levels of expression of cell surface markers associated with differentiation, aldehyde dehydrogenase activity, and glucose uptake activity. Colony formation capacity was reduced with the combined treatment of RA and B or F, and correlated with modulation of a c-Cbl/Lyn/c-Raf-centered signalsome. Combination treatment was in most cases more effective than RA alone. Based on their responses to the treatments, some primary leukemic samples cluster closer to HL-60 cells than to other primary samples, suggesting that they may represent a hitherto undefined AML subtype that is potentially responsive to RA in a combination differentiation therapy.

Reduced order modeling and analysis of the human complement system.

Complement is an important pathway in innate immunity, inflammation, and many disease processes. However, despite its importance, there are few validated mathematical models of complement activation. In this study, we developed an ensemble of experimentally validated reduced order complement models. We combined ordinary differential equations with logical rules to produce a compact yet predictive model of complement activation. The model, which described the lectin and alternative pathways, was an order of magnitude smaller than comparable models in the literature. We estimated an ensemble of model parameters from in vitro dynamic measurements of the C3a and C5a complement proteins. Subsequently, we validated the model on unseen C3a and C5a measurements not used for model training. Despite its small size, the model was surprisingly predictive. Global sensitivity and robustness analysis suggested complement was robust to any single therapeutic intervention. Only the simultaneous knockdown of both C3 and C5 consistently reduced C3a and C5a formation from all pathways. Taken together, we developed a validated mathematical model of complement activation that was computationally inexpensive, and could easily be incorporated into pre-existing or new pharmacokinetic models of immune system function. The model described experimental data, and predicted the need for multiple points of therapeutic intervention to fully disrupt complement activation.

An Effective Model of the Retinoic Acid Induced HL-60 Differentiation Program.

In this study, we present an effective model All-Trans Retinoic Acid (ATRA)-induced differentiation of HL-60 cells. The model describes reinforcing feedback between an ATRA-inducible signalsome complex involving many proteins including Vav1, a guanine nucleotide exchange factor, and the activation of the mitogen activated protein kinase (MAPK) cascade. We decomposed the effective model into three modules; a signal initiation module that sensed and transformed an ATRA signal into program activation signals; a signal integration module that controlled the expression of upstream transcription factors; and a phenotype module which encoded the expression of functional differentiation markers from the ATRA-inducible transcription factors. We identified an ensemble of effective model parameters using measurements taken from ATRA-induced HL-60 cells. Using these parameters, model analysis predicted that MAPK activation was bistable as a function of ATRA exposure. Conformational experiments supported ATRA-induced bistability. Additionally, the model captured intermediate and phenotypic gene expression data. Knockout analysis suggested Gfi-1 and PPARg were critical to the ATRAinduced differentiation program. These findings, combined with other literature evidence, suggested that reinforcing feedback is central to hyperactive signaling in a diversity of cell fate programs.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

BACKGROUND: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. RESULTS: In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. CONCLUSIONS: JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1.

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor ß (TGF-ß) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-ß induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-ß exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-ß and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-ß and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-ß treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.

Differential Contributions of Intrinsic and Extrinsic Pathways to Thrombin Generation in Adult, Maternal and Cord Plasma Samples.

BACKGROUND: Thrombin generation (TG) is a pivotal process in achieving hemostasis. Coagulation profiles during pregnancy and early neonatal period are different from that of normal (non-pregnant) adults. In this ex vivo study, the differences in TG in maternal and cord plasma relative to normal adult plasma were studied. METHODS: Twenty consented pregnant women and ten consented healthy adults were included in the study. Maternal and cord blood samples were collected at the time of delivery. Platelet-poor plasma was isolated for the measurement of TG. In some samples, anti-FIXa aptamer, RB006, or a TFPI inhibitor, BAX499 were added to elucidate the contribution of intrinsic and extrinsic pathway to TG. Additionally, procoagulant and inhibitor levels were measured in maternal and cord plasma, and these values were used to mathematically simulate TG. RESULTS: Peak TG was increased in maternal plasma (393.6±57.9 nM) compared to adult and cord samples (323.2±38.9 nM and 209.9±29.5 nM, respectively). Inhibitory effects of RB006 on TG were less robust in maternal or cord plasma (52% vs. 12% respectively) than in adult plasma (81%). Likewise the effectiveness of BAX499 as represented by the increase in peak TG was much greater in adult (21%) than in maternal (10%) or cord plasma (12%). Further, BAX499 was more effective in reversing RB006 in adult plasma than in maternal or cord plasma. Ex vivo data were reproducible with the results of the mathematical simulation of TG. CONCLUSION: Normal parturient plasma shows a large intrinsic pathway reserve for TG compared to adult and cord plasma, while TG in cord plasma is sustained by extrinsic pathway, and low levels of TFPI and AT.